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SPIRES-BOOKS: FIND KEYWORD MACHINE LEARNING *END*INIT* use /tmp/qspiwww.webspi1/26092.13 QRY 131.225.70.96 . find keyword machine learning ( in books using www Cover
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Call number:9783319463100:ONLINE Show nearby items on shelf
Title:Mathematics Across Contemporary Sciences AUS-ICMS, Sharjah, UAE, April 2015
Author(s):
Date:2017
Size:1 online resource (X, 259 p. 22 illus., 5 illus. in color p.)
Contents:1 Samirah Alsulami, Hellen Colman and Frank Neumann: The Lusternik-Schnirelmann Category for a Differentiable Stack -- 2 Richard A. Brualdi and Shi-Mei Ma: Centrosymmetric, Symmetric and Hankel-Symmetric Matrices -- 3 Costanza
Catalano and Alberto Gandolfi: Partially Independent Random Variables -- 4 Dmitry Efimov and Hana Sulieman: Sobol Sensitivity for Machine Learning -- 5 Jon Pierre Fortney: Basis Independence of Implicitly Defined Hamiltonian Circuit
Dynamics -- 6 Hichem Hajaeij: On a New Class of Variational Problems -- 7 A. Samad Hedayat: A Scientific Tour on Orthogonal Arrays -- 8 Hadi Kharaghani and Sho Suda: Hoffman’s Coclique Bound for Normal Regular Diagraphs and
Nonsymmetric Association Schemes -- 9 Salim A. Messaoudi and Soh Edwin Mukiawa: A Suspension Bridge Problem: Existence and Stability -- 10 Greg Orosi: An Interpolation-Based Approach to American Put Option Pricing -- 11 Inès Saihi:
Stable Homotopy Groups of Moore Spaces -- 12 Minjia Shi, Yiping Zhang and Patrick Solé: Notes on Quasi-Cyclic Codes with Cyclic Constituent Codes -- 13 Linda Smail and Zineb Azouz: Factorization of Computations in Bayesian Networks:
Interpretation of Factors -- 14 Abessamad Tridane, Mohamed Ali Hajji and Eduardo Mojica-Nava: Optimal Drug Treatment in a Simple Pandemic Switched System -- 15 Thomas Wunderli: On Carathéodory quasilinear functionals for BV functions
and their time flows for a dual H^1 penalty model for image restoration
ISBN:9783319463100
Series:eBooks
Series:Springer eBooks
Series:Springer 2017 package
Keywords: Mathematics , Matrix theory , Algebra , Mathematical analysis , Analysis (Mathematics) , Mathematical physics , Mathematics , Mathematical Applications in the Physical Sciences , Linear and Multilinear Algebras, Matrix Theory , Analysis
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Call number:9783319459011:ONLINE Show nearby items on shelf
Title:First Complex Systems Digital Campus World E-Conference 2015
Author(s):
Date:2017
Size:1 online resource (VIII, 424 p. 120 illus., 96 illus. in color p.)
Contents:Welcome to CS-DC’15 -- Reconstructing multi-scale dynamics -- Machine learning methods -- A formal model to compute uncertain continuous data -- Knowledge maps -- Analysis of a Planetary Scale Scientific Collaboration Dataset Reveals
Novel Patterns -- Epistemology of integrative and predictive sciences -- Information science and the complexity: are we orientated to a transdisciplinary science? -- Synthesis of ecology, biology and ethnographic data -- Bayesian
Causalities, Mappings, and Phylogenies: A Social Science Gateway for Modeling Complexity in Ethnographic, Archaeo-, Eco- and Bio-logical Variables -- Multi-level modeling -- Statistical and dynamical properties of networks -- Community
detection as an efficient way to attack real networks -- From particles to complex matter -- Chemical garden -- Assembly of molecular metal oxides from the nano to the macroscale via chemical gardens -- Physics of complex systems --
Viscosity scaling in hydrodynamic instabilities in porous media -- A general approach to the linear stability analysis of miscible viscous fingering in porous media -- From individual to social cognition -- From individual to social
cognition: Piaget, Jung and commons -- Ecological approach of sport and sport education -- Ecological Dynamics: a theoretical framework for understanding sport performance, physical education and physical -- Emerging dance movements
under ecological constraints in Contact Improvisation dancers with different background -- Emerging collective shared behaviors from individual exploration in football small-sided games -- Adaptability in swimming pattern: how do
swimmers adapt propulsive action as a function of speed? -- Backstroke start performance prediction -- Flexible perception-action strategies for follow-the-leader coordination -- Dynamic process of pulmonary data analysis: from the
athlete mouth to the coach’s hands -- From processing units to computational ecosystems to the cloud -- A multi-agent system approach to load-balancing and resource allocation for distributed computing -- Integrative science of
education -- POEM-COPA Collaborative Open Peer Assessment -- Implications of agent-based computational modeling and simulation for preventive education in children with ADHD -- MOOC as a complex system -- From fields to territories to
the planet -- Integrative logistics -- Logistics and Territory integrative approach -- Process modeling of an international transport chain through the simulation tool SIMPROCESS -- Dynamic emissions reduction from vehicles with
technical and behavioral approach -- 4p-factories (e-lab) -- Is the Lean Organisation a complex system? -- An artificial immune ecosystem model for hybrid cloud supervision -- Engineering of territory sustainability -- Spatialisation
of Soil Erosion Susceptibility Using USLE Model -- Social patterns in multicultural environments Matrimonial patterns and trans-ethnic entities -- Economics as a complex evolutionist system -- A study of heterogeneity in a stock market
simulator based on a model of agents that learn from experience in a market with multiple stocks -- Are innovation systems complex systems? -- From molecules to ecosphere -- Ocean biogeochemical dynamics -- Frontal systems as
mechanisms of fish aggregation -- Lagrangian approach to phytoplankton mesoscale biogeography in the Kerguelen region -- Lyapunov exponents and oceanic fronts.
ISBN:9783319459011
Series:eBooks
Series:Springer eBooks
Series:Springer 2017 package
Keywords: Physics , Data mining , System theory , Computational intelligence , Complexity, Computational , Physics , Complex Systems , Complex Systems , Complexity , Data Mining and Knowledge Discovery , Computational Intelligence , Biological and Medical Physics, Biophysics
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Call number:9781784394011:ONLINE Show nearby items on shelf
Title:Practical machine learning: tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques
Author(s): Sunila Gollapudi
Date:2016
Publisher:Birmingham, UK: Packt Publishing
Size:1 online resource
ISBN:9781784394011
Series:eBook
Series:EBL eBook
Keywords: Machine learning
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Call number:9781783553365:ONLINE Show nearby items on shelf
Title:Python Data Analysis learn how to apply powerful data analysis techniques with popular open source Python modules
Author(s): Ivan Idris
Date:2014
Publisher:Birmingham, UK: Packt Publishing
Size:348 p
Note:online access: non linear lending
Contents:Getting started with Python libraries -- NumPy arrays -- Statistics and linear algebra -- pandas primer -- Retrieving, processing, and storing data -- Data visualization -- Signal processing and time series -- Working with databases -- Analyzing tex tual data and social media -- Predictive analytics and machine learning -- Environments outside the Python ecosystem and cloud computing -- Performance tuning, profiling, and concurrency -- Appendix A : key concepts -- Appendix B : useful functions -- Ap p endix C : online resources
ISBN:9781783553365
Series:eBooks
Series:EBL eBook
Keywords: Python (Computer program language)
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Call number:9781119245759:ONLINE Show nearby items on shelf
Title:Machine learning for dummies
Author(s): John Mueller
Date:2016
Publisher:Hoboken, NJ : Wiley
Size:1 online resource (435 p.)
ISBN:9781119245513
Series:eBooks
Series:EBL eBook
Keywords: Machine learning.
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Call number:1848214227:ONLINE Show nearby items on shelf
Title:Data Mining and Machine Learning in Building Energy Analysis: Towards High Performance Computing
Author(s): Magoules
Date:2016
Publisher:Wiley-ISTE
Size:1 online resource (187 p.)
ISBN:9781848214224
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Computer Science
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Call number:1848212038:ONLINE Show nearby items on shelf
Title:Semi-Supervised and Unsupervised Machine Learning: Novel Strategies
Author(s): Albalate
Date:2011
Publisher:Wiley-ISTE
Size:1 online resource (321 p.)
ISBN:9781848212039
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Electrical & Electronics Engineering
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Call number:1119994330:ONLINE Show nearby items on shelf
Title:Structural Health Monitoring - A Machine Learning Perspective
Author(s): Farrar
Date:2012
Publisher:Wiley
Size:1 online resource (655 p.)
ISBN:9781119994336
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Civil Engineering & Construction
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Call number:1118961749:ONLINE Show nearby items on shelf
Title:Machine Learning in Python:for Predictive Analysis
Author(s): Bowles
Date:2015
Publisher:Wiley
Size:1 online resource (361 p.)
ISBN:9781118961742
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Computer Science
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Call number:1118889061:ONLINE Show nearby items on shelf
Title:Machine Learning: Hands-On for Developers and Technical Professionals
Author(s): Bell
Date:2014
Publisher:Wiley
Size:1 online resource (409 p.)
ISBN:9781118889060
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:1118745671:ONLINE Show nearby items on shelf
Title:Financial Signal Processing and Machine Learning
Author(s): Akansu
Date:2016
Publisher:Wiley-IEEE Press
Size:1 online resource (321 p.)
ISBN:9781118745670
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Electrical & Electronics Engineering
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Call number:1118618041:ONLINE Show nearby items on shelf
Title:Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
Author(s): Dean
Date:2014
Publisher:Wiley
Size:1 online resource (289 p.)
ISBN:9781118618042
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Business & Management
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Call number:111836208X:ONLINE Show nearby items on shelf
Title:Multi-Agent Machine Learning: A Reinforcement Approach
Author(s): Schwartz
Date:2014
Publisher:Wiley
Size:1 online resource (257 p.)
ISBN:9781118362082
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Electrical & Electronics Engineering
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Call number:1118115147:ONLINE Show nearby items on shelf
Title:Modern Machine Learning Techniques and their Applications in Cartoon Animation Research
Author(s): Yu
Date:2013
Publisher:Wiley-IEEE Press
Size:1 online resource (209 p.)
ISBN:9781118115145
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Electrical & Electronics Engineering
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Call number:047091999X:ONLINE Show nearby items on shelf
Title:Reinforcement and Systemic Machine Learning for Decision Making
Author(s): Kulkarni
Date:2012
Publisher:Wiley-IEEE Press
Size:1 online resource (313 p.)
ISBN:9780470919996
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Electrical & Electronics Engineering
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Call number:0470663057:ONLINE Show nearby items on shelf
Title:Machine Learning in Image Steganalysis
Author(s): Schaathun
Date:2012
Publisher:Wiley
Size:1 online resource (297 p.)
ISBN:9780470663059
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Electrical & Electronics Engineering
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Call number:0470195150:ONLINE Show nearby items on shelf
Title:Statistical and Machine Learning Approaches for Network Analysis
Author(s): Dehmer
Date:2012
Publisher:Wiley
Size:1 online resource (345 p.)
ISBN:9780470195154
Series:eBooks
Series:Wiley Online Library
Series:Wiley 2016 package purchase
Keywords: Statistics
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Call number:SPRINGER-2016-9784431559788:ONLINE Show nearby items on shelf
Title:Information Geometry and Its Applications
Author(s): Shun-ichi Amari
Date:2016
Edition:1st ed. 2016
Size:1 online resource (373 p.)
Note:10.1007/978-4-431-55978-8
Contents:1 Manifold, Divergence and Dually Flat Structure -- 2 Exponential Families and Mixture Families of Probability -- 3 Invariant Geometry of Manifold of Probability -- 4 α-Geometry, Tsallis q-Entropy and Positive-Definite -- 5 Elements of Differentia l Geometry -- 6 Dual Affine Connections and Dually Flat Manifold -- 7 Asymptotic Theory of Statistical Inference -- 8 Estimation in the Presence of Hidden Variables -- 9 Neyman–Scott Problem -- 10 Linear Systems and Time Series -- 11 Machine Learning -- 12 Natural Gradient Learning and its Dynamics in Singular -- 13 Signal Processing and Optimization -- Index
ISBN:9784431559788
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Applied Mathematical Sciences: 194
Keywords: Mathematics , Computer science , Computer mathematics , Differential geometry , Statistics , Mathematics , Differential Geometry , Mathematical Applications in Computer Science , Statistical Theory and Methods
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Call number:SPRINGER-2014-9783319015958:ONLINE Show nearby items on shelf
Title:Data Analysis, Machine Learning and Knowledge Discovery [electronic resource]
Author(s): Myra Spiliopoulou
Lars Schmidt-Thieme
Ruth Janning
Date:2014
Publisher:Cham : Springer International Publishing : Imprint: Springer
Size:1 online resource
Note:Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be appliedto a vast set of app lications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning andknowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012
Contents:AREA Statistics and Data Analysis: Classifcation, Cluster Analysis, Factor Analysis and Model Selection
AREA Machine Learning and Knowledge Discovery: Clustering, Classifiers, Streams and Social Networks
AREA Data Analysis and Classification in Marketing
AREA Data Analysis in Finance
AREA Data Analysis in Biostatistics and Bioinformatics
AREA Interdisciplinary Domains: Data Analysis in Music, Education and Psychology
LIS Workshop: Workshop on Classification and Subject Indexing in Library and Information Science
ISBN:9783319015958
Series:eBooks
Series:SpringerLink
Series:Studies in Classification, Data Analysis, and Knowledge Organization, 1431-8814
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Statistical methods , Mathematical statistics , Marketing , Philosophy (General)
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Call number:SPRINGER-2013-9781461474289:ONLINE Show nearby items on shelf
Title:Statistical Methods for Dynamic Treatment Regimes [electronic resource] : Reinforcement Learning, Causal Inference, and Personalized Medicine
Author(s): Bibhas Chakraborty
Erica E.M Moodie
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. Thisvolume demonstrates thes e methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medicalparadigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, andtechnical reports with the goal of orienting researchers to the fiel d. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementarycalculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where codedoes not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowled ge of statistical programming could implement the methods from scratch. This will be an important volume for a widerange of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also findmaterial in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies
Note:Springer eBooks
Contents:Introduction
The Data: Observational Studies and Sequentially Randomized Trials
Statistical Reinforcement Learning
Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes
Estimation of Optimal DTRs by Directly Modeling Regimes
G
computation: Parametric Estimation of Optimal DTRs
Estimation DTRs for Alternative Outcome Types
Inference and Non
regularity
Additional Considerations and Final Thoughts
Glossary
Index
References
ISBN:9781461474289
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Medical records Data processing
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Call number:SPRINGER-2013-9781461471387:ONLINE Show nearby items on shelf
Title:An Introduction to Statistical Learning [electronic resource] with Applications in R
Author(s): Gareth James
Daniela Witten
Trevor Hastie
Robert Tibshirani
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biologyto finance to market ing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification,resampling methods, shrinkage approaches, tr ee-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is tofacilitate the use of these statistical learning techniques by pract itioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular opensource statistical software platform. Two of the authors co-wrote The Elements of Statistic al Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. AnIntroduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader a udience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statisticallearning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra
Note:Springer eBooks
Contents:Introduction
Statistical Learning
Linear Regression
Classification
Resampling Methods
Linear Model Selection and Regularization
Moving Beyond Linearity
Tree
Based Methods
Support Vector Machines
Unsupervised Learning
Index
ISBN:9781461471387
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X : v103
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2013-9781461450764:ONLINE Show nearby items on shelf
Title:Mathematical Methodologies in Pattern Recognition and Machine Learning [electronic resource] : Contributions from the International Conference on Pattern Recognition Applications and Methods, 2012
Author(s): Pedro Latorre Carmona
J. Salvador Snchez
Ana L.N Fred
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Springer eBooks
ISBN:9781461450764
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Proceedings in Mathematics & Statistics, 2194-1009 : v30
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Computer science , Optical pattern recognition , Systems theory , Mathematical optimization
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Call number:SPRINGER-2013-9781441998781:ONLINE Show nearby items on shelf
Title:Robust Data Mining [electronic resource]
Author(s): Petros Xanthopoulos
Panos M Pardalos
Theodore B Trafalis
Date:2013
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques.Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This briefcontains an overview of the rapidly growing field ofrobust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems.Thisbrief will appeal to theoreticians and data miners working in thi s field
Note:Springer eBooks
Contents:1. Introduction
2. Least Squares Problems
3. Principal Component Analysis
4. Linear Discriminant Analysis
5.Support Vector Machines
6. Conclusion
ISBN:9781441998781
Series:e-books
Series:SpringerLink (Online service)
Series:SpringerBriefs in Optimization, 2190-8354
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Software engineering , Data mining , Mathematical optimization
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Call number:SPRINGER-2012-9783642288821:ONLINE Show nearby items on shelf
Title:Self-Evolvable Systems [electronic resource] : Machine Learning in Social Media
Author(s): Octavian Iordache
Date:2012
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This monograph presents key method to successfully manage the growing complexity of systems where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limitsand new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is highlighted, self-properties such as self-organization, self-configuration, and self-repairing are introduced andchallenges and limitations of the self-evolvable engineering systems are evaluated
Note:Springer eBooks
Contents:Introduction
General Framework
Differential Models
Informational Criteria
Self
Evolvability for Physical and Chemical Systems
Self
Evolvability for Biosystems
Self
Evolvability for Cognitive Systems
Control Systems
Manufacturing Systems
Concept Lattices
Design of Experiments
Perspectives
ISBN:9783642288821
Series:e-books
Series:SpringerLink (Online service)
Series:Understanding Complex Systems, 1860-0832
Series:Physics and Astronomy (Springer-11651)
Keywords: Engineering
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Call number:SPRINGER-2012-9783642288210:ONLINE Show nearby items on shelf
Title:Essays in Mathematics and its Applications [electronic resource] : In Honor of Stephen Smales 80th Birthday
Author(s): Panos M Pardalos
Themistocles M Rassias
Date:2012
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:The volume is dedicated to Stephen Smale on the occasion of his 80th birthday. Besides his startling 1960 result of the proof of the Poincar conjecture for all dimensions greater than or equal to five, Smales ground breakingcontributions invarious fi elds in Mathematics have marked the second part of the 20th century and beyond. Stephen Smale has done pioneering work in differential topology, global analysis, dynamical systems, nonlinear functionalanalysis, numerical analysis, theory of computation an d machine learning as well as applications in the physical and biological sciences and economics. In sum, Stephen Smale has manifestly broken the barriers among the different fieldsof mathematics and dispelled some remaining prejudices. He is indeed a uni versal mathematician. Smale has been honored with several prizes and honorary degrees including, among others, the Fields Medal(1966), The Veblen Prize (1966),the National Medal of Science (1996) and theWolf Prize (2006/2007)
Note:Springer eBooks
Contents:Preface
Transitivity and Topological Mixing for C1 Diffeomorphisms
Recent Results on the Size of Critical Sets
The FoxLi operator as a Test and a Spur for WienerHopf Theory
Kahler Metrics with Cone Singularities along a Divisor
The Space of Framed functions is Contractible
Quantum Gravity via Manifold Positivity
Parabolic Explosions in Families of Complex Polynomials
Super Stable Kahlerian Horseshoe?
A Smooth Multivariate Interpolation Algorithm
Bifurcations of Solutions of the 2
Dimensional Navier
Stokes System
Arnold Diffusion by Variational Methods
Turnin
ISBN:9783642288210
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Mathematical optimization , Operations research
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Call number:SPRINGER-2012-9783642272257:ONLINE Show nearby items on shelf
Title:Bayesian Methods in Structural Bioinformatics [electronic resource]
Author(s): Thomas Hamelryck
Kanti Mardia
Jesper Ferkinghoff-Borg
Date:2012
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction,simulation, experimenta l structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networksor support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum ofmathematical knowledge. Therefore, the book includes introductory c hapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics
Note:Springer eBooks
Contents:Part I Foundations: An Overview of Bayesian Inference and Graphical Models
Monte Carlo Methods for Inferences in High
dimensional Systems
Part II Energy Functions for Protein Structure Prediction: On the Physical Relevance and Statistical Interpretation of Knowledge based Potentials
Statistical Machine Learning of Protein Energetics from Experimentally Observed Structures
A Statistical View on the Reference Ratio Method
Part III Directional Statistics and Shape Theory: Statistical Modelling and Simulation Using the Fisher
Bingham Distribution
Statistics of Bivariate von Mises
ISBN:9783642272257
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Medicine , Bioinformatics
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Call number:SPRINGER-2012-9783642249396:ONLINE Show nearby items on shelf
Title:Lectures on Gaussian Processes [electronic resource]
Author(s): Mikhail Lifshits
Date:2012
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables. Their theory presents a powerful range of tools for probabilistic modelling in various academic andtechnical domains such as Stati stics, Forecasting, Finance, Information Transmission, Machine Learning - to mention just a few. The objective of these Briefs is to present a quick and condensed treatment of the core theory that areader must understand in order to make his own independe nt contributions. The primary intended readership are PhD/Masters students and researchers working in pure or applied mathematics. The first chapters introduce essentials of theclassical theory of Gaussian processes and measures with the core notions of r eproducing kernel, integral representation, isoperimetric property, large deviation principle. The brevity being a priority for teaching and learningpurposes, certain technical details and proofs are omitted. The later chapters touch important recent issu es not sufficiently reflected in the literature, such assmall deviations, expansions, and quantization of processes.Inuniversity teaching, one can build a one-semester advanced course upon these Briefs
Note:Springer eBooks
Contents:Preface
1.Gaussian Vectors and Distributions
2.Examples of Gaussian Vectors, Processes and Distributions
3.Gaussian White Noise and Integral Representations
4.Measurable Functionals and the Kernel
5.Cameron
Martin Theorem
6.Isoperimetric Inequality
7.Measure Concavity and Other Inequalities
8.Large Deviation Principle
9.Functional Law of the Iterated Logarithm
10.Metric Entropy and Sample Path Properties
11.Small Deviations
12.Expansions of Gaussian Vectors
13.Quantization of Gaussian Vectors
14.Invitation to Further Reading
References
ISBN:9783642249396
Series:e-books
Series:SpringerLink (Online service)
Series:SpringerBriefs in Mathematics, 2191-8198
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Distribution (Probability theory)
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Call number:SPRINGER-2012-9783642202537:ONLINE Show nearby items on shelf
Title:The Playful Machine [electronic resource] Theoretical Foundation and Practical Realization of Self-Organizing Robots
Author(s): Ralf Der
Georg Martius
Date:2012
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complexmachines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? The book provides answers bydeveloping a general principle---homeokinesis, the dynam ical symbiosis between brain, body, and environment---that is shown to drive robots to self- determined, individual development in a playful and obviously embodiment- related way:a dog-like robot starts playing with a barrier, eventually jumping or climbi ng over it a snakebot develops coiling and jumping modes humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarioswhen encountering an opponent. The book also develops guided self-organization, a new method that he lps to make the playful machines fit for fulfilling tasks in the real world. The book provides two levels of presentation. Students andscientific researchers interested in the field of robotics, self-organization and dynamical systems theory may be satisf ied by the in-depth mathematical analysis of the principle, the bootstrapping scenarios, and the emergingbehaviors. But the book additionally comes with a robotics simulator inviting also the non- scientific reader to simply enjoy the fabulous world of pl ayful machines by performing the numerous experiments
Note:Springer eBooks
Contents:1.Introduction
2.Self
Organization in Nature and Machines
3.The Sensorimotor Loop
4.Principles of Self
Regulation
Homeostasis
5.A General Approach to Self
Organization
Homeokinesis
6.From Fixed
Point Flows to Hysteresis Oscillators
7.Symmetries, Resonances, and Second Order Hysteresis
8.Low Dimensional Robotic Systems
9.Model Learning
10.High
Dimensional Robotic Systems
11.Facing the Unknown
Homeokinesis in a New Representation*
12.Guided Self
Organization
A First Realization
13.Channeling Self
Organization
14.Reward
Driven Self
Organization
1
ISBN:9783642202537
Series:e-books
Series:SpringerLink (Online service)
Series:Cognitive Systems Monographs, 1867-4925 : v15
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Artificial intelligence
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Call number:SPRINGER-2012-9781461421078:ONLINE Show nearby items on shelf
Title:Data Mining for Biomarker Discovery [electronic resource]
Author(s): Panos M Pardalos
Petros Xanthopoulos
Michalis Zervakis
Date:2012
Publisher:Boston, MA : Springer US
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics,medicine, and anyone interest ed in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targetedtherapy. Moreover, the discovery and management o f new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics. This volume is acollection of state-of-the-artresearch from selectparticipants of the International Conference on Biomedical Data and Knowledge Minin g: Towards Biomarker Discovery, held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrievalmethods, and statistical machine learning techniques, all presented with new results, models, and algorithms
Note:Springer eBooks
Contents:Preface
1. Data Mining Strategies Applied in Brain Injury Models (S. Mondello, F. Kobeissy, I. Fingers, Z. Zhang, R.L. Hayes, K.K.W. Wang)
Application of Decomposition Methods in the Filtering of Event Related Potentials (K. Michalopoulos, V. Iordanidou, M. Zervakis)
3. EEG Features as Biomarkers for Discrimination of Pre
ictal states (A. Tsimpiris, D. Kugiumtzis)
4. Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Non
epileptic Seizure and Complex Partial Seizure Patients (J.H. Chien, D
S. Shiau, J.C. Sackellares, J.J. Halford, K.M. Kelly, P.M. Pardalos)
ISBN:9781461421078
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Optimization and Its Applications, 1931-6828 : v65
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Biochemical engineering , Medical records Data processing , Data mining
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Call number:SPRINGER-2011-9783642221477:ONLINE Show nearby items on shelf
Title:Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems [electronic resource] : cole dt de Probabilits de Saint-Flour XXXVIII-2008
Author(s): Vladimir Koltchinskii
Date:2011
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, therehave been new developmen ts in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysisof these problems are concentration and devi ation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chainingbounds). Sparse recovery based on l_1-type penalization and low ra nk matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalizedempirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful
Note:Springer eBooks
ISBN:9783642221477
Series:e-books
Series:SpringerLink (Online service)
Series:Lecture Notes in Mathematics, 0075-8434 : v2033
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Distribution (Probability theory)
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Call number:SPRINGER-2011-9781441999610:ONLINE Show nearby items on shelf
Title:Geometric Methods and Applications [electronic resource] : For Computer Science and Engineering
Author(s): Jean Gallier
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This book is an introduction to the fundamental concepts and tools needed for solving problems of a geometric nature using a computer. It attempts to fill the gap between standard geometry books, which are primarily theoretical,and applied books on c omputer graphics, computer vision, robotics, or machine learning. This book covers the following topics: affine geometry, projective geometry, Euclidean geometry, convex sets, SVD and principal componentanalysis, manifolds and Lie groups, quadratic optimi zation, basics of differential geometry, and a glimpse of computational geometry (Voronoi diagrams and Delaunay triangulations). Some practical applications of the concepts presentedin this book include computer vision, more specifically contour grouping, motion interpolation, and robot kinematics. In this extensively updated second edition, more material on convex sets, Farkass lemma, quadraticoptimization and the Schur complement have been added. The chapter on SVD has been greatly expanded and now incl udes a presentation of PCA. The book is well illustrated and has chapter summaries and a large number of exercisesthroughout. It will be of interest to a wide audience including computer scientists, mathematicians, and engineers. Reviews of first edition: Gallier's book will be a useful source for anyone interested in applications ofgeometrical methods to solve problems that arise in various branches of engineering. It may help to develop the sophisticated concepts from the more advanced parts of geometry into useful tools for applications. (MathematicalReviews, 2001) ...it will be useful as a reference book for postgraduates wishing to find the connection between their current problem and the underlying geometry. (The Australian Mathematical Society, 200 1)
Note:Springer eBooks
Contents:Introduction
Basics of Affine Geometry
Basic Properties of Convex Sets
Embedding an Affine Space in a Vector Space
Basics of Projective Geometry
Basics of Euclidean Geometry
Separating and Supporting Hyperplanes Polar Duality
Polytopes and Polyhedra
The CartanDieudonne Theorem
The Quaternions and the Spaces S3, SU(2), SO(3), and RP3
DirichletVoronoi Diagrams
Basics of Hermitian Geometry
Spectral Theorems
Singular Value Decomposition (SVD) and Polar Form
Applications of SVD and Pseudo
Inverses
Quadratic Optimization Problems
Schur
ISBN:9781441999610
Series:e-books
Series:SpringerLink (Online service)
Series:Texts in Applied Mathematics, 0939-2475 : v38
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Computer vision , Geometry , Mathematical optimization
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Call number:SPRINGER-2011-9781441997821:ONLINE Show nearby items on shelf
Title:Targeted Learning [electronic resource] : Causal Inference for Observational and Experimental Data
Author(s): Mark J van der Laan
Sherri Rose
Date:2011
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever data sets are massive, often measuring hundreds of thousands of measurements for a single subject.The fieldis ready to move towards c lear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjectivechoices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at bothstatisticians and applied researchers interested in causal inference and ge neral effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihoodestimator, including related concepts necessary to understand and apply these methods. Parts II-IX h andle complex data structures and topics applied researchers will immediately recognize from their own research, includingtime-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, a nd genomic studies. Targeted Learning, by Mark J. van der Laan and Sherri Rose, fills a much neededgap in statistical and causal inference. It protects us from wasting computational, analytical, and data resources on irrelevant aspects of a problem and te aches us how to focus on what is relevant answering questions thatresearchers truly care about. -Judea Pearl, Computer Science Department, University of California, Los Angeles In summary, this book should be on the shelf of every investigator who conduc ts observational research and randomizedcontrolled trials. The concepts and methodology are foundational for causal inference and at the same time stay true to what the data at hand can say a
Note:Springer eBooks
Contents:Models, Inference, and Truth
The Open Problem
Defining the Model and Parameter
Super Learning
Introduction to TMLE
Understanding TMLE
Why TMLE?
Bounded Continuous Outcomes
Direct Effects and Effect Among the Treated
Marginal Structural Models
Positivity
Robust Analysis of RCTs Using Generalized Linear Models
Targeted ANCOVA Estimator in RCTs
Independent Case
Control Studies
Why Match? Matched Case
Control Studies
Nested Case
Control Risk Score Prediction
Super Learning for Right
Censored Data
RCTs with Time
to
Event Outcomes
RCTs with Time
ISBN:9781441997821
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2011-9781441996343:ONLINE Show nearby items on shelf
Title:Probability for Statistics and Machine Learning [electronic resource] : Fundamentals and Advanced Topics
Author(s): Anirban DasGupta
Date:2011
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in anextremely accessible sty le, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in itsunification of probability and statistics, its cov erage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year longgraduate course in statistics, computer science, or mathematics, for self-stud y, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory,asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processe s, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayesestimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probabilit y
Note:Springer eBooks
Contents:Chapter 1. Review of Univariate Probability
Chapter 2. Multivariate Discrete Distributions
Chapter 3. Multidimensional Densities
Chapter 4. Advance Distribution Theory
Chapter 5. Multivariate Normal and Related Distributions
Chapter 6. Finite Sample Theory of Order Statistics and Extremes
Chapter 7. Essential Asymptotics and Applications
Chapter 8. Characteristic Functions and Applications
Chapter 9. Asymptotics of Extremes and Order Statistics
Chapter 10. Markov Chains and Applications
Chapter 11. Random Walks
Chapter 12. Brownian Motion and Gaussian Processes
ISBN:9781441996343
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer simulation , Bioinformatics , Distribution (Probability theory) , Mathematical statistics
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Call number:SPRINGER-2011-9780817649043:ONLINE Show nearby items on shelf
Title:Towards an Information Theory of Complex Networks [electronic resource] : Statistical Methods and Applications
Author(s): Matthias Dehmer
Frank Emmert-Streib
Alexander Mehler
Date:2011
Edition:1
Publisher:Boston : Birkhuser Boston
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:For over a decade, complex networks have steadily grown as an important tool across abroad array of academic disciplines, with applications ranging from physics to social media.A tightly organizedcollectionofcarefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statisticalmodels of complex networks in the natural sciences and humanities.The book'smajor goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand andcharacterize real-world networks. This volume is the first to present a self-contained, comprehensive overvi ew of information-theoretic modelsof complex networks with an emphasis on applications. It begins with four chaptersdeveloping the most significant formal-theoretical issues of network modeling,butthe majority of the book is devoted tocombining theoretica l results with an empirical analysis of real networks. Specific topics include: chemicalgraph theory ecosystem interaction dynamics social ontologies language networks software systems Thiswork marks a first step toward establishing advanced statistical i nformation theory as a unified theoretical basis of complexnetworksfor allscientific disciplines. As such, itcan serve asa valuable resource foradiverse audience of advanced students and professional scientists.It is primarilyintendedas a reference for re search, but couldalso be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others
Note:Springer eBooks
Contents:Preface
Entropy of Digraphs and Infinite Networks
An Information
Theoretic Upper Bound on Planar Graphs Using Well
orderly Maps
Probabilistic Inference Using Function Factorization and Divergence Minimization
Wave Localization on Complex Networks
Information
Theoretic Methods in Chemical Graph Theory
On the Development and Application of Net
Sign Graph Theory
The Central Role of Information Theory in Ecology
Inferences About Coupling from Ecological Surveillance Monitoring
Markov Entropy Centrality
Social Ontologies as Generalizedd Nearly Acyclic Directed Graphs
ISBN:9780817649043
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Coding theory , Artificial intelligence , Physiology Mathematics , Telecommunication
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Call number:SPRINGER-2011-9780817647896:ONLINE Show nearby items on shelf
Title:Structural Analysis of Complex Networks [electronic resource]
Author(s): Matthias Dehmer
Date:2011
Publisher:Boston : Birkhuser Boston
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Because of the increasing complexity and growth of real-world networks, their analysis by using classical graph-theoretic methods is oftentimes a difficult procedure. As a result, there is a strong need to combine graph-theoreticmethods with mathemat ical techniques from other scientific disciplines, such as machine learning and information theory, in order to analyze complex networks more adequately. Filling a gap in literature, this self-contained bookpresents theoretical and application-oriented re sults to structurally explore complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a toolfor solving interdisciplinary problems. Special emphasis is given to m ethods related to the following areas: * Applications to biology, chemistry, linguistics, and data analysis * Graph colorings * Graph polynomials * Informationmeasures for graphs * Metrical properties of graphs * Partitions and decompositions * Quantitati ve graph measures Structural Analysis of Complex Networks is suitable for a broad, interdisciplinary readership of researchers,practitioners, and graduate students in discrete mathematics, statistics, computer science, machine learning, artificial intelli gence, computational and systems biology, cognitive science, computational linguistics, and mathematicalchemistry. The book may be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods
Note:Springer eBooks
Contents:Preface
A Brief Introduction to Complex Networks and Their Analysis
Partitions of Graphs
Distance in Graphs
Domination in Graphs
Spectrum and Entropy for Infinite Directed Graphs
Application of Infinite Labeled Graphs to Symbolic Dynamical Systems
Decompositions and Factorizations of Complete Graphs
Geodetic Sets in Graphs
Graph Polynomials and Their Applications I: The Tutte Polynomial
Graph Polynomials and Their Applications II: Interrelations and Interpretations
Reconstruction Problems for Graphs, Krawtchouk Polynomials, and Diophantine Equations
Subgraph
ISBN:9780817647896
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Computer Communication Networks , Computational complexity , Data mining , Bioinformatics , Combinatorics
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Call number:SPRINGER-2010-9789048123223:ONLINE Show nearby items on shelf
Title:geoENV VII Geostatistics for Environmental Applications [electronic resource]
Author(s): P. M Atkinson
C. D Lloyd
Date:2010
Publisher:Dordrecht : Springer Netherlands : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This volume brings together selected contributions from geoENV 2008, the 7th International Conference on Geostatistics for Environmental Applications, held in Southampton, UK, in September 2008. This book presents thestate-of-the-art in geostatistics for the environmental sciences. It includes a wide range of methodological advances and applications. It offers insight and guidance for researchers, professionals, graduate students and others seekinginformation on the latest perspectives in the field. The rich body of applications will enable those new to geostatistics to assess the utility of the methods for their own applications. The book includes 35 chapters on topics asdiverse as methodological developments, applications in the soil sciences, clim atology, pollution, health, wildlife mapping, fisheries and remote sensing, amongst other areas. With its focus on environmental applications ofgeostatistics, rather than the more traditional geostatistical remit of mining and petroleum exploration, this book is part of a series that presents an invaluable resource. This book will be a first port of call for those who wish toapply geostatistical methods in the environmental sciences. Audience: Researchers, scientists, professionals, institutes, libraries, graduate students of geosciences, geostatistics, spatial statistics, environmental science andengineering, ecology, oceanography, climatology, hydrology, soil and forestry science
Note:Springer eBooks
Contents:Preface
Geostatistical modelling of wildlife populations: a non
stationary hierarchical model for count data
Incorporating survey data to improve space
time geostatistical analysis of King Prawn catch rate
Multivariate interpolation of monthly precipitation amount in the United Kingdom
Extreme precipitation modelling using geostatistics and machine learning algorithms
On geostatistical analysis of rainfall using data from boundary sites
Geostatistics Applied to the City of Porto Urban Climatology
Integrating Meteorological Dynamic Data and Historical Data into a Stochastic
ISBN:9789048123223
Series:e-books
Series:SpringerLink (Online service)
Series:Quantitative Geology and Geostatistics, 0924-1973 : v16
Series:Mathematics and Statistics (Springer-11649)
Keywords: Geography , Environmental sciences
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Call number:SPRINGER-2010-9781441969446:ONLINE Show nearby items on shelf
Title:Frontiers of Statistical Decision Making and Bayesian Analysis [electronic resource] : In Honor of James O. Berger
Author(s): Ming-Hui Chen
Peter Mller
Dongchu Sun
Keying Ye
Dipak K Dey
Date:2010
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This bookprovides a review of c urrent research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objectiveBayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods forcategorical and spatio-temporal data analysis and posterior simulation met hods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modelingand applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical andapplied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian stat istics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians whomake use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and re search scholars in statistics or biostatistics who wish to acquaint themselves with currentresearch frontiers. Ming-Hui Chen is Professor of Statistics at the University of Connecticut Dipak K. Dey is Head and Professor of Statistics at the University of Connecticut Peter Mller is Professor of Biostatistics at theUniversity of Texas M. D. Anderson Cancer Center Dongchu Sun is Professor of Statistics at the University of Missouri- Columbia and Keyi
Note:Springer eBooks
Contents:Objective Bayesian Inference with Applications
Bayesian Decision Based Estimation and Predictive Inference
Bayesian Model Selection and Hypothesis Tests
Bayesian Inference for Complex Computer Models
Bayesian Nonparametrics and Semi
parametrics
Bayesian Influence and Frequentist Interface
Bayesian Clinical Trials
Bayesian Methods for Genomics, Molecular and Systems Biology
Bayesian Data Mining and Machine Learning
Bayesian Inference in Political Science, Finance, and Marketing Research
Bayesian Categorical Data Analysis
Bayesian Geophysical, Spatial and Tempora
ISBN:9781441969446
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Mathematical statistics
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Call number:SPRINGER-2009-9780387981857:ONLINE Show nearby items on shelf
Title:Functional Data Analysis with R and MATLAB [electronic resource]
Author(s): James Ramsay
Giles Hooker
Spencer Graves
Date:2009
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those whowould like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlablanguages for a set of data analyses that show case functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the detailsof key functions within these pages. This book is accompanied by add itional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package forR includes script files to reproduce nearly all the examples in the book including all bu t one of the 76 figures. Jim Ramsay is Professor Emeritus at McGill University and is an international authority on many aspects of multivariateanalysis. He was President of the Statistical Society of Canada in 2002-3 and holds the Societys Gold Medal for his work in functional data analysis. His statistical work draws on his collaboration with researchers in biomechanics,chemical engineering, climatology, ecology, economics, human biology, medicine and psychology. Giles Hooker is Assistant Professor of B iological Statistics and Computational Biology at Cornell University. His research interests includestatistical inference in nonlinear dynamics, machine learning and computational statistics. Spencer Graves is an engineer with a PhD in Statistics and over 15 years experience using S-Plus and R to analyze data in a broad range ofapplications. He has made substantive contributions to several CRAN packages including fda and DierckxSpline.
Note:Springer eBooks
Contents:Introduction to functional data analysis
Essential comparisons of the Matlab and R languages
How to specify basis systems for building functions
How to build functional data objects
Smoothing: Computing curves from noisy data
Descriptions of functional data
Exploring variation: Functional principal and canonical components analysis
Registration: Aligning features for samples of curves
Functional linear models for scalar responses
Linear models for functional responses
Functional models and dynamics
ISBN:9780387981857
Series:e-books
Series:SpringerLink (Online service)
Series:Use R
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Statistical methods , Mathematical statistics , Marketing , Psychometrics
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Call number:SPRINGER-2009-9780387981543:ONLINE Show nearby items on shelf
Title:Solar Image Analysis and Visualization [electronic resource]
Author(s): J Ireland
C. A Young
Date:2009
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This volume presents a selection of papers on the state of the art of image enhancement, automated feature detection, machine learning, and visualization tools in support of solar physics that focus on the challenges presented bynew ground-based and space-based instrumentation. The articles and topics were inspired by the Third Solar Image Processing Workshop, held at Trinity College Dublin, Ireland but contributions from other experts have been included aswell. This book is mainly aimed at researche rs and graduate students working on image processing and compter vision in astronomy and solar physics
Note:Springer eBooks
Contents:Preface: A Topical Issue on Solar Image Analysis and Visualization
FESTIVAL: A Multiscale Visualization Tool for Solar Imaging Data
Visualization of Distributed Solar Data and Metadata with the Solar Weather Browser
Widespread Occurrence of Trenching Patterns in the Granulation Field: Evidence for Roll Convection?
Principal Components and Independent Component Analysis of Solar and Space Data
Automatic Recognition and Characterisation of Supergranular Cells from Photospheric Velocity Fields
Automated McIntosh
Based Classification of Sunspot Groups Using MDI Images
Multifra
ISBN:9780387981543
Series:e-books
Series:SpringerLink (Online service)
Series:Physics and Astronomy (Springer-11651)
Keywords: Computer vision
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Call number:SPRINGER-2009-9780387981352:ONLINE Show nearby items on shelf
Title:Principles and Theory for Data Mining and Machine Learning [electronic resource]
Author(s): Bertrand Clarke
Ernest Fokoue
Hao Helen Zhang
Date:2009
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression,classification, and ensemble m ethods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and thistreatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While theorientation is conceptual and theoretical, the main points are regularly reinf orced by computational comparisons. Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students,this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples,complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researc hers who want a conceptual overview of the central topics in data mining and machine learning. Bertrand Clarkeis a Professor of Statistics in the Department of Medicine, Department of Epidemiology and Public Health, and the Center for Computational Scienc es at the University of Miami. He has been on the Editorial Board of the Journal of theAmerican Statistical Association, the Journal of Statistical Planning and Inference, and Statistical Papers. He is co-winner, with Andrew Barron, of the 1990 Browder J. Thompson Prize from the Institute of Electrical and ElectronicEngineers. Ernest Fokoue is an Assistant Professor of Statistics at Kettering University. He has also taught at Ohio State University and b
Note:Springer eBooks
Contents:Variability, information, prediction
Kernel smoothing
Spline smoothing
New wave nonparametrics
Supervised learning: Partition methods
Alternative nonparametrics
Computational comparisons
Unsupervised learning: Clustering
Learning in high dimensions
Variable selection
Multiple testing
ISBN:9780387981352
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer science , Data mining , Optical pattern recognition , Bioinformatics , Mathematical statistics
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Call number:SPRINGER-2009-9780387848587:ONLINE Show nearby items on shelf
Title:The Elements of Statistical Learning [electronic resource] Data Mining, Inference, and Prediction
Author(s): Trevor Hastie
Robert Tibshirani
Jerome Friedman
Date:2009
Edition:Second Edition
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge ofunderstanding these data ha s led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are oftenexpressed with different terminology. This book d escribes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples aregiven, with a liberal use of color graphics. It is a valuable resource f or statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervisedlearning. The many topics include neural networks, support vector machines, classification tre es and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in theoriginal, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in thisarea: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling softw are and environment in R/S-PLUS and invented principal curves andsurfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inve
Note:Springer eBooks
Contents:Overview of Supervised Learning
Linear Methods for Regression
Linear Methods for Classification
Basis Expansions and Regularization
Kernel Smoothing Methods
Model Assessment and Selection
Model Inference and Averaging
Additive Models, Trees, and Related Methods
Boosting and Additive Trees
Neural Networks
Support Vector Machines and Flexible Discriminants
Prototype Methods and Nearest
Neighbors
Unsupervised Learning
Random Forests
Ensemble Learning
Undirected Graphical Models
High
Dimensional Problems: p ? N
ISBN:9780387848587
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Data mining , Artificial intelligence , Bioinformatics , Biology Data processing , Mathematical statistics
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Call number:SPRINGER-2009-9780387096247:ONLINE Show nearby items on shelf
Title:Reactive Search and Intelligent Optimization [electronic resource]
Author(s): Roberto Battiti
Mauro Brunato
Franco Mascia
Date:2009
Publisher:Boston, MA : Springer US
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive search self-tunes and adapts,effectively learning by d oing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models,experimental algorithms applied to optimization, intell igent tuning and design of heuristics. Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt todevelop some fresh intuition for the approaches. The book looks at differen t optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems,problems are introduced wherever they help make the discussion more concrete, or when a specific proble m has been widely studied by reactive search and intelligent optimization heuristics. Individual chapters cover reacting on theneighborhood reacting on the annealing schedule reactive prohibitions model-based search reacting on the objective function rela tionships between reactive search and reinforcement learning and much more. Each chapter isstructured to show basic issues and algorithms the parameters critical for the success of the different methods discussed and opportunities and schemes for the auto mated tuning of these parameters. Anyone working in decision makingin business, engineering, economics or science will find a wealth of information here
Note:Springer eBooks
Contents:Introduction: Machine Learning for Intelligent Optimization
Reacting on the neighborhood
Reacting on the Annealing Schedule
Reactive Prohibitions
Reacting on the Objective Function
Reacting on the Objective Function
Supervised Learning
Reinforcement Learning
Algorithm Portfolios and Restart Strategies
Racing
Teams of Interacting Solvers
Metrics, Landscapes and Features
Open Problems
ISBN:9780387096247
Series:e-books
Series:SpringerLink (Online service)
Series:Operations Research/Computer Science Interfaces Series, 1387-666X : v45
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Electronic data processing , Artificial intelligence , Operations research , Engineering mathematics , Industrial engineering
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Call number:SPRINGER-2008-9780387781891:ONLINE Show nearby items on shelf
Title:Modern Multivariate Statistical Techniques [electronic resource] : Regression, Classification, and Manifold Learning
Author(s): Alan J Izenman
Date:2008
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the HumanGenome Project has op ened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The authortakes a broad perspective for the first time i n a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multipleregression, principal components, canonical variates, linear discrimina nt analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit,neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, bo osting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another uniquefeature of this book is the discussion of database management systems. This book is appropriate for advanced undergradu ate students, graduate students, and researchers in statistics, computer science, artificial intelligence,psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and pro bability and statistics is required. The book presents a carefully-integrated mixtureof theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as e xamples in the book, over 200 exercises, and many colorillustrations and photographs. Alan J. Izenman is Professor of Statistics and Director of the Center for Statistical and Information Science at Te
Note:Springer eBooks
Contents:and Preview
Data and Databases
Random Vectors and Matrices
Nonparametric Density Estimation
Model Assessment and Selection in Multiple Regression
Multivariate Regression
Linear Dimensionality Reduction
Linear Discriminant Analysis
Recursive Partitioning and Tree
Based Methods
Artificial Neural Networks
Support Vector Machines
Cluster Analysis
Multidimensional Scaling and Distance Geometry
Committee Machines
Latent Variable Models for Blind Source Separation
Nonlinear Dimensionality Reduction and Manifold Learning
Correspondence Analysis
ISBN:9780387781891
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer science , Data mining , Optical pattern recognition , Bioinformatics , Mathematical statistics
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Call number:SPRINGER-2008-9780387772400:ONLINE Show nearby items on shelf
Title:Bioconductor Case Studies [electronic resource]
Author(s): Florian Hahne
Wolfgang Huber
Robert Gentleman
Seth Falcon
Date:2008
Edition:1
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. Inthis volume, the auth ors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include * import and preprocessing of data from various sources * statisticalmodeling of differential gene expression * biolog ical metadata * application of graphs and graph rendering * machine learning for clustering and classification problems * gene set enrichment analysis Each chapter of this book describesan analysis of real data using hands-on example driven approaches. Sh ort exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executedon a local computer, and readers are able to reproduce every computation, figure, and table . The authors of this book have longtime experience in teaching introductory and advanced courses to the application of Bioconductor software.Florian Hahne is a Postdoc at the Fred Hutchinson Cancer Research Center in Seattle, developing novel methodologi es for the analysis of high-throughput cell-biological data. Wolfgang Huber is a research group leader in the EuropeanMolecular Biology Laboratory at the European Bioinformatics Institute in Cambridge. He has wide-ranging experience in the development of methods for the analysis of functional genomics experiments. Robert Gentleman is Head of theProgram in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle, and he is one of the two authors of the original R system. Seth Falcon i s a member of the R core team and former project manager and developerfor the Bioconductor project
Note:Springer eBooks
Contents:The ALL Dataset
R and Bioconductor Introduction
Processing Affymetrix Expression Data
Two Color Arrays
Fold Changes, Log Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization
Easy Differential Expression
Differential Expression
Annotation and Metadata
Supervised Machine Learning
Unsupervised Machine Learning
Using Graphs for Interactome Data
Graph Layout
Gene Set Enrichment Analysis
Hypergeometric Testing Used for Gene Set Enrichment Analysis
Solutions to Exercises
ISBN:9780387772400
Series:e-books
Series:SpringerLink (Online service)
Series:Use R!
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Bioinformatics , Biology Mathematics
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Call number:SPRINGER-2007-9783540465515:ONLINE Show nearby items on shelf
Title:Algorithms for Approximation [electronic resource] : Proceedings of the 5th International Conference, Chester, July 2005
Author(s): Armin Iske
Jeremy Levesley
Date:2007
Publisher:Berlin, Heidelberg : Springer Berlin Heidelberg
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Approximation methods are vital in many challenging applications of computational science and engineering. This is a collection of papers from world experts in a broad variety of relevant applications, including patternrecognition, machine learning, multiscale modelling of fluid flow, metrology, geometric modelling, tomography, signal and image processing. It documents recent theoretical developments which have lead to new trends in approximation, itgives important computational aspects and multidisc iplinary applications, thus making it a perfect fit for graduate students and researchers in science and engineering who wish to understand and develop numerical algorithms for thesolution of their specific problems. An important feature of the book is th at it brings together modern methods from statistics, mathematical modelling and numerical simulation for the solution of relevant problems, with a wide rangeof inherent scales. Contributions of industrial mathematicians, including representatives from Mi crosoft and Schlumberger, foster the transfer of the latest approximation methods to real-world applications
Note:Springer eBooks
Contents:Imaging and Data Mining
Ranking as Function Approximation
Two Algorithms for Approximation in Highly Complicated Planar Domains
Computational Intelligence in Clustering Algorithms, With Applications
Energy
Based Image Simplification with Nonlocal Data and Smoothness Terms
Multiscale Voice Morphing Using Radial Basis Function Analysis
Associating Families of Curves Using Feature Extraction and Cluster Analysis
Numerical Simulation
Particle Flow Simulation by Using Polyharmonic Splines
Enhancing SPH using Moving Least
Squares and Radial Basis Functions
Stepwise Calc
ISBN:9783540465515
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Computer science , Functions, special , Computer science Mathematics , Engineering mathematics
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Call number:SPRINGER-2006-9781402049958:ONLINE Show nearby items on shelf
Title:Intelligent Algorithms in Ambient and Biomedical Computing [electronic resource]
Author(s): Wim Verhaegh
Emile Aarts
Jan Korst
Date:2006
Publisher:Dordrecht : Springer Netherlands
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:The rapid growth in electronic systems in the past decade has boosted research in the area of computational intelligence. As it has become increasingly easy to generate, collect, transport, process, and store huge amounts of data,the role of intellig ent algorithms has become prominent in order to visualize, manipulate, retrieve, and interpret the data. For instance, intelligent search techniques have been developed to search for relevant items in hugecollections of web pages, and data mining and inte rpretation techniques play a very important role in making sense out of huge amounts of biomolecular measurements. As a result, the added value of many modern systems is no longerdetermined by hardware only, but increasingly by the intelligent software th at supports and facilitates the user in realizing his or her objectives. This book is the outcome of a series of discussions at the Philips Symposium onIntelligent Algorithms, which was held in Eindhoven in December 2004. It contains many exciting and pra ctical examples of the use of intelligent algorithms in the areas of ambient and biomedical computing. It contains topics such asbioscience computing, database design, machine consciousness, scheduling, video summarization, audio classification, semantic reasoning, machine learning, tracking & localization, secure computing, and communication
Note:Springer eBooks
Contents:Part I Healthcare
1. Bioscience Computing and the Role of Computational Simulation in Biology
2. The Many Strands of DNA Computing
3. Bio
Inspired Data Management
4. An Introduction to Machine Consciousness
Part II Lifestyle
5. Optimal Selection of TV Shows for Watching and Recording
6. Movie
in
a
Minute: Automatically Generated Video Previews
7. Features for Audio Classification: Percussiveness References
8. Extracting the Key from Music
9. Approximate Semantic Matching of Music Classes on the Internet
10. Ontology
Based Information Extraction from the World Wid
ISBN:9781402049958
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Multimedia systems , Artificial intelligence , Biology Data processing , Algorithms
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Call number:SPRINGER-2006-9780387449562:ONLINE Show nearby items on shelf
Title:Selected Papers of Frederick Mosteller [electronic resource]
Author(s): Stephen E Fienberg
David C Hoaglin
Date:2006
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume brings together 40 of his most original and influential papers, capturing thevariety and depth of hi s writings. The editors hope to share these with a new generation of researchers, so that they can build upon his insights and efforts. This volume of selected papers is a companion to the earlier volume AStatistical Model: Frederick Mosteller's Contribut ions to Statistics, Science, and Public Policy, edited by Stephen E. Fienberg, David C. Hoaglin, William H. Kruskal, and Judith M. Tanur (Springer-Verlag, 1990), and to Mosteller'sforthcoming autobiography, which will also be published by Springer-Verlag. It includes a biography and a comprehensive bibliography of Mosteller's books, papers, and other writings. Stephen E. Fienberg is Maurice Falk UniversityProfessor of Statistics and Social Science, in the Departments of Statistics and Machine Learning at Carnegie Mellon University, Pittsburgh, PA. David C. Hoaglin is Principal Scientist at Abt Associates Inc., Cambridge, MA
Note:Springer eBooks
Contents:Frederick MostellerA Brief Biography
Unbiased Estimates for Certain Binomial Sampling Problems with Applications
On Some Useful Inefficient Statistics
A k
Sample Slippage Test for an Extreme Population
The Uses and Usefulness of Binomial Probability Paper
The Education of a Scientific Generalist
Remarks on the Method of Paired Comparisons: I. The Least Squares Solution Assuming Equal Standard Deviations and Equal Correlations
Remarks on the Method of Paired Comparisons: II. The Effect of an Aberrant Standard Deviation When Equal Standard Deviations and Equal Corre
ISBN:9780387449562
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Series in Statistics, 0172-7397
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Educational tests and measurements , Mathematical statistics , Econometrics , Psychometrics
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Call number:SPRINGER-2005-9780387293622:ONLINE Show nearby items on shelf
Title:Bioinformatics and Computational Biology Solutions Using R and Bioconductor [electronic resource]
Author(s): Robert Gentleman
Vincent J Carey
Wolfgang Huber
Rafael A Irizarry
Sandrine Dudoit
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted inthe open source statis tical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data frommicroarray, proteomic, and flow cytometry platfo rms curation and delivery of biological metadata for use in statistical modeling and interpretation statistical analysis of high-throughput data, including machine learning andvisualization, modeling and visualization of graphs and networks. The developer s of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data,and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text it is adynamic document. Code underlying all of the computations that are shown is made available on a companion website, and re aders can reproduce every number, figure, and table on their own computers. Robert Gentleman is Head of theProgram in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle. He is one of the two authors of the original R system and a leading member of the R core team. Vincent Carey is Associate Professor of Medicine(Biostatistics), Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School. Gentleman and Carey are co-founders of the Bioconductor project. Wolfgang Hub er is Group Leader in the European Molecular Biology Laboratory atthe European Bioinformatics Institute in Cambridge. He has made influential contributions to the error modeling of microarray data. Raf
Note:Springer eBooks
Contents:Preprocessing data from genomic experiments
Preprocessing Overview
Preprocessing High
density Oligonucleotide Arrays
Quality Assessment of Affymetrix GeneChip Data
Preprocessing Two
Color Spotted Arrays
Cell
Based Assays
SELDI
TOF Mass Spectrometry Protein Data
Meta
data: biological annotation and visualization
Meta
data Resources and Tools in Bioconductor
Querying On
line Resources
Interactive Outputs
Visualizing Data
Statistical analysis for genomic experiments
Analysis Overview
Distance Measures in DNA Microarray Data Analysis
Cluster Analysis of
ISBN:9780387293622
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Bioinformatics , Animal genetics
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Call number:SPRINGER-2005-9780387283562:ONLINE Show nearby items on shelf
Title:Search Methodologies [electronic resource] : Introductory Tutorials in Optimization and Decision Support Techniques
Author(s): Edmund K Burke
Graham Kendall
Date:2005
Publisher:Boston, MA : Springer US
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Search Methodologies is a tutorial survey of the methodologies that are at the confluence of several fields: Computer Science, Mathematics and Operations Research. It is a carefully structured and integrated treatment of themajor technologies in opti mization and search methodology. The book is made up of 19 chapters. The chapter authors are drawn from across Computer Science and Operations Research and include some of the worlds leading authorities intheir field. The result is a major state-of-the-ar t tutorial text of the main optimization and search methodologies available to researchers, students and practitioners across discipline domains in applied science. It can be used as atextbook or a reference book to learn and apply these methodologies to a wide range of todays problems. It has been written by some of the worlds most well known authors in the field
Note:Springer eBooks
Contents:Classical Techniques
Integer Programming
Genetic Algorithms
Genetic Programming
Tabu Search
Simulated Annealing
Variable Neighborhood Search
Constraint Programming
Multi
Objective Optimization
Complexity Theory and the No Free Lunch Theorem
Machine Learning
Artificial Immune Systems
Swarm Intelligence
Fuzzy Reasoning
Rough Set Based Decision Support
Hyper
Heuristics
Approximation Algorithms
Fitness Landscapes
ISBN:9780387283562
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Electronic data processing , Mathematical optimization , Operations research , Engineering mathematics
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Call number:SPRINGER-2005-9780387276564:ONLINE Show nearby items on shelf
Title:Statistical and Inductive Inference by Minimum Message Length [electronic resource]
Author(s): C.S Wallace
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:The Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation ofOccam's Razor, asserts t hat the best explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data. This book givesa sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practicalapplication. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophyof Science. Statistical and Inductive Inference by Minimum Message Length will be of special in terest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing tomake use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underly ing theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in agraduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining. C.S. Wallace was appointed Fou ndation Chair of Computer Science at Monash University in 1968, at the age of 35, where he workeduntil his death in 2004. He received an ACM Fellowship in 1995, and was appointed Professor Emeritus in 1996. Professor Wallace made numerous significant cont ributions to diverse areas of Computer Science, such as ComputerArchitecture, Simulation and Machine Learning. His final research focused primarily on the Minimum Message Length Principle
Note:Springer eBooks
Contents:From the contents: Inductive Inference
Information
Strict Minimum Message Length (SMML)
Approximations to SMML
MML: Quadratic Approximations to SMML
MML Details in Some Interesting Cases
Structural Models
The Feathers on the Arrow of Time
MML as a Descriptive Theory
Related Work
Bibliography. Index
ISBN:9780387276564
Series:e-books
Series:SpringerLink (Online service)
Series:Information Science and Statistics, 1613-9011
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Coding theory , Computer science , Artificial intelligence , Mathematical statistics
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Call number:SPRINGER-2005-9780387245553:ONLINE Show nearby items on shelf
Title:Statistical Modeling and Analysis for Complex Data Problems [electronic resource]
Author(s): Pierre Duchesne
Bruno RMillard
Date:2005
Publisher:Boston, MA : Springer US
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of todays more complex problems and it reflects some of the important research directions in the field. Twenty-nine authorslargely from MontrealsGERAD Multi-University Research C enter and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processespresent survey chapters on various theoretical and applied problems ofimportance and interest to researchers and students across a numbe r of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida,data mining, estimation of uncertainty for machine learning algorithms, interacting stoc hastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statisticaltesting in genetics and for dependent data, statistical analysis of time series analysis, theoretical and appl ied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. Thebook examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and pro vides direction for further research exploration of the area
Note:Springer eBooks
Contents:Foreword
Contributing Authors
Preface
Dependence Properties of Meta
Elliptical Distributions
The Statistical Significance of Palm Beach County
Bayesian Functional Estimation of Hazard Rates for Randomly Right Censored Data Using Fourier Series Methods
Conditions for the Validity of F
Ratio Tests for Treatment and Carryover Effects in Crossover Designs
Bias in Estimating the Variance of K
Fold Cross Validation
Effective Construction of Modified Histograms in Higher Dimensions
On Robust Diagnostics at Individual Lags Using RA
ARX Estimators
Bootstrap Confidence Inte
ISBN:9780387245553
Series:e-books
Series:SpringerLink (Online service)
Series:Mathematics and Statistics (Springer-11649)
Keywords: Mathematics , Computer science , Distribution (Probability theory) , Mathematical statistics , Statistics
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Call number:SPRINGER-2004-9781441990525:ONLINE Show nearby items on shelf
Title:Networks of Learning Automata Techniques for Online Stochastic Optimization
Author(s): M. A. L Thathachar
Date:2004
Size:1 online resource (268 p.)
Note:10.1007/978-1-4419-9052-5
Contents:1. Introduction -- 1.1 Machine Intelligence and Learning -- 1.2 Learning Automata -- 1.3 The Finite Action Learning Automaton (FALA) -- 1.4 Some Classical Learning Algorithms -- 1.5 The Discretized Probability FALA -- 1.6 The
Continuous Action Learning Automaton (CALA) -- 1.7 The Generalized Learning Automaton (GLA) -- 1.8 The Parameterized Learning Automaton (PLA) -- 1.9 Multiautomata Systems -- 1.10 Supplementary Remarks -- 2. Games of Learning Automata
-- 2.1 Introduction -- 2.2 A Multiple Payoff Stochastic Game of Automata -- 2.3 Analysis of the Automata Game Algorithm -- 2.4 Game with Common Payoff -- 2.5 Games of FALA -- 2.6 Common Payoff Games of CALA -- 2.7 Applications -- 2.8
Discussion -- 2.9 Supplementary Remarks -- 3. Feedforward Networks -- 3.1 Introduction -- 3.2 Networks of FALA -- 3.3 The Learning Model -- 3.4 The Learning Algorithm -- 3.5 Analysis -- 3.6 Extensions -- 3.7 Convergence to the Global
Maximum -- 3.8 Networks of GLA -- 3.9 Discussion -- 3.10 Supplementary Remarks -- 4. Learning Automata for Pattern Classification -- 4.1 Introduction -- 4.2 Pattern Recognition -- 4.3 Common Payoff Game of Automata for PR -- 4.4
Automata Network for Pattern Recognition -- 4.5 Decision Tree Classifiers -- 4.6 Discussion -- 4.7 Supplementary Remarks -- 5. Parallel Operation of Learning Automata -- 5.1 Introduction -- 5.2 Parallel Operation of FALA -- 5.3
Parallel Operation of CALA -- 5.4 Parallel Pursuit Algorithm -- 5.5 General Procedure -- 5.6 Parallel Operation of Games of FALA -- 5.7 Parallel Operation of Networks of FALA -- 5.8 Discussion -- 5.9 Supplementary Remarks -- 6. Some
Recent Applications -- 6.1 Introduction -- 6.2 Supervised Learning of Perceptual Organization in Computer Vision -- 6.3 Distributed Control of Broadcast Communication Networks -- 6.4O ther Applications -- 6.5 Discussion -- Epilogue --
Appendices -- A The ODE Approach to Analysis of Learning Algorithms -- A.I Introduction -- A.2 Derivation of the ODE Approximation -- A.2.1 Assumptions -- A.2.2 Analysis -- A.3 Approximating ODEs for Some Automata Algorithms -- A.3.2
The CALA Algorithm -- A.3.3 Automata Team Algorithms -- A.4 Relaxing the Assumptions -- B Proofs of Convergence for Pursuit Algorithm -- B.1 Proof of Theorem 1.1 -- B.2 Proof of Theorem 5.7 -- C Weak Convergence and SDE Approximations
-- C.I Introduction -- C.2 Weak Convergence -- C.3 Convergence to SDE -- C.3.1 Application to Global Algorithms -- C.4 Convergence to ODE -- References
ISBN:9781441990525
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Physics , Operations research , Decision making , Computer science , Artificial intelligence , Computational linguistics , Statistical physics , Dynamical systems , Physics , Statistical Physics, Dynamical Systems and Complexity , Artificial Intelligence (incl. Robotics) , Language Translation and Linguistics , Operation Research/Decision Theory , Computer Science, general
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Call number:SPRINGER-2003-9783662055946:ONLINE Show nearby items on shelf
Title:Adaptivity and Learning An Interdisciplinary Debate
Author(s):
Date:2003
Size:1 online resource (403 p.)
Note:10.1007/978-3-662-05594-6
Contents:Adaptivity and Learning — an Interdisciplinary Debate -- I Biology and Behaviour of Adaptation and Learning -- Biology of Adaptation and Learning -- The Adaptive Properties of the Phosphate Uptake System of Cyanobacteria: Information
Storage About Environmental Phosphate Supply -- Cognitive Architecture of a Mini-Brain -- Cerebral Mechanisms of Learning Revealed by Functional Neuroimaging in Humans -- Creating Presence by Bridging Between the Past and the Future:
the Role of Learning and Memory for the Organization of Life -- II Physics Approach to Learning — Neural Networks and Statistics -- The Physics Approach to Learning in Neural Networks -- Statistical Physics of Learning and
Generalization -- The Statistical Physics of Learning: Phase Transitions and Dynamical Symmetry Breaking -- The Complexity of Learning with Supportvector Machines — A Statistical Physics Study -- III Mathematical Models of Learning --
Mathematics Approach to Learning -- Learning and the Art of Fault-Tolerant Guesswork -- Perspectives on Learning Symbolic Data with Connectionistic Systems -- Statistical Learning and Kernel Methods -- Inductive Versus Approximative
Learning -- IV Learning by Experience -- Learning by Experience -- Learning by Experience from Others — Social Learning and Imitation in Animals and Robots -- Reinforcement Learning: a Brief Overview -- A Simple Model for Learning from
Unspecific Reinforcement -- V Human-Like Cognition and AI Learning -- Aspects of Human-Like Cognition and AI Learning -- Making Robots Learn to See -- Using Machine Learning Techniques in Complex Multi-Agent Domains -- Learning
Similarities for Informally Defined Objects -- Semiotic Cognitive Information Processing: Learning to Understand Discourse. A Systemic Model of Meaning Constitution
ISBN:9783662055946
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Physics , Philosophy , Computers , Evolutionary biology , Statistical physics , Dynamical systems , Control engineering , Robotics , Mechatronics , Physics , Physics, general , Statistical Physics, Dynamical Systems and Complexity , Control, Robotics, Mechatronics , Computing Methodologies , Philosophy, general , Evolutionary Biology
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Call number:SPRINGER-2003-9780387215792:ONLINE Show nearby items on shelf
Title:Nonlinear Estimation and Classification
Author(s):
Date:2003
Size:1 online resource (477 p.)
Note:10.1007/978-0-387-21579-2
Contents:I Longer Papers -- 1 Wavelet Statistical Models and Besov Spaces -- 2 Coarse-to-Fine Classification and Scene Labeling -- 3 Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets -- 4 Traffic Flow
on a Freeway Network -- 5 Internet Traffic Tends Toward Poisson and Independent as the Load Increases -- 6 Regression and Classification with Regularization -- 7 Optimal Properties and Adaptive Tuning of Standard and Nonstandard
Support Vector Machines -- 8 The Boosting Approach to Machine Learning: An Overview -- 9 Improved Class Probability Estimates from Decision Tree Models -- 10 Gauss Mixture Quantization: Clustering Gauss Mixtures -- 11 Extended Linear
Modeling with Splines -- II Shorter Papers -- 12 Adaptive Sparse Regression -- 13 Multiscale Statistical Models -- 14 Wavelet Thresholding on Non-Equispaced Data -- 15 Multi-Resolution Properties of Semi-Parametric Volatility Models --
16 Confidence Intervals for Logspline Density Estimation -- 17 Mixed-Effects Multivariate Adaptive Splines Models -- 18 Statistical Inference for Simultaneous Clustering of Gene Expression Data -- 19 Statistical Inference for
Clustering Microarrays -- 20 Logic Regression — Methods and Software -- 21 Adaptive Kernels for Support Vector Classification -- 22 Generalization Error Bounds for Aggregate Classifiers -- 23 Risk Bounds for CART Regression Trees -- 24
On Adaptive Estimation by Neural Net Type Estimators -- 25 Nonlinear Function Learning and Classification Using RBF Networks with Optimal Kernels -- 26 Instability in Nonlinear Estimation and Classification: Examples of a General
Pattern -- 27 Model Complexity and Model Priors -- 28 A Strategy for Compression and Analysis of Very Large Remote Sensing Data Sets -- 29 Targeted Clustering of Nonlinearly Transformed Gaussians -- 30 Unsupervised Learning of Curved
Manifolds -- 31 ANOVA DDP Models: A Review
ISBN:9780387215792
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Lecture Notes in Statistics: 171
Keywords: Statistics , Statistics , Statistical Theory and Methods
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Call number:SPRINGER-2002-9789401003247:ONLINE Show nearby items on shelf
Title:Advances in Computational Intelligence and Learning Methods and Applications
Author(s):
Date:2002
Size:1 online resource (515 p.)
Note:10.1007/978-94-010-0324-7
Contents:Accuracy and Transparency of Fuzzy Systems -- Should Tendency Assessment Precede Rule Extraction by Clustering? (No!) -- A Review of Wavelet Networks, Wavenets, Fuzzy Wavenets and their Applications -- Investigating Neural Network
Efficiency and Structure by Weight Investigation -- An Evaluation of Confidence Bound Estimation Methods for Neural Networks -- Compensation of Periodic Disturbances in Continuous Processing Plants by Means of a Neural Controller --
Predictive Control with Restricted Genetic Optimisation -- Adaptive Parameterization of Evolutionary Algorithms and Chaotic Populations -- Neuro-Fuzzy Systems for Rule-Based Modelling of Dynamic Processes -- Hybrid Intelligent
Architectures using a Neurofuzzy Approach -- Unifying Learning with Evolution Through Baldwinian Evolution and Lamarckism -- Using An Evolutionary Strategy to Select Input Features for a Neural Network Classifier -- Advances in Machine
Learning -- Symbolic and Neural Learning of Named-Entity Recognition and Classification Systems in Two Languages -- Fuzzy Model-Based Reinforcement Learning -- A Cellular Space for Feature Extraction and Classification -- A Fuzzy
Approach to Taming the Bullwhip Effect -- Forecast of Short Term Trends in Stock Exchange using Fuzzy Rules and Neural Networks on Multiresolution Processed Signals -- Customer Relationship Management: A Combined Approach by Customer
Segmentation and Database Marketing -- A new Vendor Evaluation Product for SAP RJ3® Systems -- About Robustness of Fuzzy Logic PD and PID Controller under Changes of Reasoning Methods -- Control of MIMO Dead Time Processes Using Fuzzy
Relational Models -- Fuzzy Sliding Mode Controllers Synthesis through Genetic Optimization -- Fuzzy RED: Congestion Control for TCP/IP Diff-Serv -- The Use of Reinforcement Learning Algorithms in Traffic Control of High Speed Networks
-- Fuzzy Reasoning in WCDMA Radio Resource Functions -- Odour Classification based on Computational Intelligence Techniques -- Fuzzy Rule Based System for Diagnosis of Stone Construction Cracks of Buildings -- Automated Design of
Multi-Drilling Gear Machines -- Optimal Design of Alloy Steels Using Genetic Algorithms -- Intelligent Systems in Biomedicine -- Diagnosis Of Aphasia Using Neural And Fuzzy Techniques -- Gene Expression Data Mining for Functional
Genomics using Fuzzy Technology -- Symbolic, Neural and Neuro-fuizy Approaches to Pattern Recognition in Cardiotocograms -- Perspectives ofComputational Intelligence
ISBN:9789401003247
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:International Series in Intelligent Technologies: 18
Keywords: Mathematics , Operations research , Decision making , Artificial intelligence , Mathematical logic , Mathematics , Mathematical Logic and Foundations , Artificial Intelligence (incl. Robotics) , Operation Research/Decision Theory
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Call number:SPRINGER-2002-9780387227337:ONLINE Show nearby items on shelf
Title:From Computer to Brain Foundations of Computational Neuroscience
Author(s): William W Lytton
Date:2002
Size:1 online resource (364 p.)
Note:10.1007/b98859
Contents:Perspectives -- Computational Neuroscience and You -- Basic Neuroscience -- Computers -- Computer Representations -- The Soul of an Old Machine -- Cybernetics -- Concept Neurons -- Neural Coding -- Our Friend the Limulus -- Supervised
Learning: Delta Rule and Back-Propagation -- Associative Memory Networks -- Brains -- From Soap to Volts -- Hodgkin-Huxley Model -- Compartment Modeling -- From Artificial Neural Network to Realistic Neural Network -- Neural Circuits
-- The Basics
ISBN:9780387227337
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Mathematics , Neurosciences , Computers , Artificial intelligence , Neurobiology , Applied mathematics , Engineering mathematics , Biomedical engineering , Mathematics , Applications of Mathematics , Neurosciences , Artificial Intelligence (incl. Robotics) , Neurobiology , Biomedical Engineering , Computing Methodologies
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Call number:SPRINGER-2000-9789401706063:ONLINE Show nearby items on shelf
Title:Abduction and Induction Essays on their Relation and Integration
Author(s):
Date:2000
Size:1 online resource (309 p.)
Note:10.1007/978-94-017-0606-3
Contents:1 Abductive and inductive reasoning: background and issues -- 2 Smart inductive generalizations are abductions -- 3 Abduction as epistemic change: a Peircean model in Artificial Intelligence -- 4 Abduction: between conceptual richness
and computational complexity -- 5 On relationships between induction and abduction: a logical point of view -- 6 On the logic of hypothesis generation -- 7 Abduction and induction from a non-monotonic reasoning perspective -- 8 Unified
inference in extended syllogism -- 9 On the relations between abductive and inductive explanation -- 10 Learning, Bayesian probability, graphical models, and abduction -- 11 On the relation between abductive and inductive hypotheses --
12 Integrating abduction and induction in Machine Learning -- 13 Abduction and induction combined in a metalogic framework -- 14 Learning abductive and nonmonotonic logic programs -- 15 Cooperation of abduction and induction in Logic
Programming -- 16 Abductive generalization and specialization -- 17 Using abduction for induction based on bottom generalization
ISBN:9789401706063
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Applied Logic Series: 18
Keywords: Mathematics , Logic , Philosophy and science , Numerical analysis , Artificial intelligence , Mathematical logic , Mathematics , Mathematical Logic and Foundations , Logic , Artificial Intelligence (incl. Robotics) , Numeric Computing , Philosophy of Science
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Call number:SPRINGER-1998-9781475728453:ONLINE Show nearby items on shelf
Title:Managing in Uncertainty: Theory and Practice
Author(s):
Date:1998
Size:1 online resource (541 p.)
Note:10.1007/978-1-4757-2845-3
Contents:A review of country risk assessment approaches: New empirical evidence -- Political risk and stock market development -- Construction of a simplified index of country risk: The case of Europe -- The importance of order for the
decision in uncertainty -- Business and economic education — criteria for choice of studies and student expectations -- Chaotic oscillations in real economic time series data: Evaluation of logistic model fit and forecasting
performance -- Evaluation of a neuro-fuzzy scheme forecasting exchange rates -- Economics of energy and treatment of uncertainty to incorporate environmental considerations in investment decisions -- Mergers in the Spanish savings
banks and their presence in the market: A cause-effect analysis on an autonomous scale -- On distributions for stock returns: A survey of empirical investigations -- A case study of use of artificial options in the Athens Stock
Exchange -- Multicriteria decision aid in credit cards assessment -- Memory-based advertising effectiveness techniques: Recall versus recognition -- Controlling uncertainty in a Spanish national and European setting faced with illicit
advertising -- Theory and practice about risk in the incorrect management associations between store image and private label products in Spanish supermarkets -- The effectiveness of the shopwindow and its relationship with the types of
consumer purchase. An empirical study -- Identifying consumer’s preferences using artificial neural network techniques -- Market simulations via rule induction: A machine learning approach -- Artificial neural networks systems for
multiple criteria decision making -- Study of determinant factors in the associationism of the franchisors in Spain -- Franchising: All around the world -- Forecasting in marketing planning. Forecasting performance of the logistic
model and applications of S-4 model -- Implementation of a performance measurement framework in Greek manufacture: An empirical research -- Dialectic approach of risks’ perception (the case of prefecture of Thessaloniki) --
Investigation of the urban risk system of Thessaloniki’s city complex -- Manager motivation facing the imponderable of uncertainty -- Application of multivariate techniques to assess the relationship between organizational culture and
innovation process -- The establishment of cooperative agreements among SMEs: An appropriate way to reduce uncertainty -- Firms facing uncertainty: The cooperation option -- Social economy organizations in a world in transition --
Total quality management in action: Implementing ways in Spanish companies -- Budgetary control based on activities cost, total quality and ISO 9000 norms -- Causes of changes in top management -- Part-time work in Europe: A review of
major trends -- Unknown loss in Spain: Concern about the distribution sector -- Author Index
ISBN:9781475728453
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Applied Optimization: 19
Keywords: Business , Marketing , Management , Operations research , Decision making , Finance , Business and Management , Management , Finance, general , Marketing , Operation Research/Decision Theory
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Call number:SPRINGER-1998-9781461554738:ONLINE Show nearby items on shelf
Title:Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach
Author(s): Bilal M Ayyub
Date:1998
Size:1 online resource (371 p.)
Note:10.1007/978-1-4615-5473-8
Contents:1. The Role of Constrained Fuzzy Arithmetic in Engineering -- 2. General Perspective on the Formalization of Uncertain Knowledge -- 3. Distributional Representations of Random Interval Measurements -- 4. A Fuzzy Morphology: a Logical
Approach -- 5. Reliability Analysis with Fuzziness and Randomness -- 6. Fuzzy Signal Detection with Multiple Waveform Features -- 7. Uncertainty Modeling of Normal Vibrations -- 8. Modeling and Implementation of Fuzzy Time Point
Reasoning in Microprocessor Systems -- 9. Model Learning with Bayesian Networks for Target Recognition -- 10. System Life Cycle Optimization Under Uncertainty -- 11. Valuation-Based Systems for Pavement Management Decision Making --
12. Hybrid Least-Square Regression Analysis -- 13. Linear Regression with Random Fuzzy Numbers -- 14. Neural Net Solutions to Systems of Fuzzy Linear Equations -- 15. Fuzzy Logic: A Case Study in Performance Measurement -- 16. Fuzzy
Genetic Algorithm Based Approach to Machine Learning Under Uncertainty -- 17. Recurrent Neuro-Fuzzy Models of Complex Systems -- 18. Adaptive Fuzzy Systems with Sinusoidal Membership Functions -- 19. A Computational Method for Fuzzy
Optimization -- 20. Interaction of Fuzzy Knowledge Granules for Conjunctive Logic -- 21. Fuzzy Decision Processes with Expected Fuzzy Rewards -- 22. On the Computability of Possibilistic Reliability -- 23. Distributed Reasoning with
Uncertain Data -- 24. A Fresh Perspective on Uncertainty Modeling: Uncertainty vs. Uncertainty Modeling -- About the Editors
ISBN:9781461554738
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:International Series in Intelligent Technologies: 11
Keywords: Computer science , Operations research , Decision making , Artificial intelligence , Mathematical logic , Calculus of variations , Computer Science , Artificial Intelligence (incl. Robotics) , Mathematical Logic and Foundations , Calculus of Variations and Optimal Control Optimization , Operation Research/Decision Theory
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Call number:SPRINGER-1997-9783642979668:ONLINE Show nearby items on shelf
Title:Self-Organizing Maps
Author(s): Teuvo Kohonen
Date:1997
Edition:Second Edition
Size:1 online resource (426 p.)
Note:10.1007/978-3-642-97966-8
Contents:1. Mathematical Preliminaries -- 1.1 Mathematical Concepts and Notations -- 1.2 Distance Measures for Patterns -- 1.3 Statistical Pattern Recognition -- 1.4 The Subspace Methods of Classification -- 1.5 The Robbins-Monro Stochastic
Approximation -- 1.6 Dynamically Expanding Context -- 2. Justification of Neural Modeling -- 2.1 Models, Paradigms, and Methods -- 2.2 On the Complexity of Biological Nervous Systems -- 2.3 Relation Between Biological and Artificial
Neural Networks -- 2.4 What Functions of the Brain Are Usually Modeled? -- 2.5 When Do We Have to Use Neural Computing? -- 2.6 Transformation, Relaxation, and Decoder -- 2.7 Categories of ANNs -- 2.8 Competitive-Learning Networks --
2.9 Three Phases of Development of Neural Models -- 2.10 A Simple Nonlinear Dynamic Model of the Neuron -- 2.11 Learning Laws -- 2.12 Brain Maps -- 3. The Basic SOM -- 3.1 The SOM Algorithm in the Euclidean Space -- 3.2 The
“Dot-Product SOM” -- 3.3 Preliminary Demonstrations of Topology-Preserving Mappings -- 3.4 Basic Mathematical Approaches to Self-Organization -- 3.5 Initialization of the SOM Algorithms -- 3.6 On the “Optimal” Learning-Rate Factor --
3.7 Effect of the Form of the Neighborhood Function -- 3.8 Magnification Factor -- 3.9 Practical Advice for the Construction of Good Maps -- 3.10 Examples of Data Analyses Implemented by the SOM -- 3.11 Using Gray Levels to Indicate
Clusters in the SOM -- 3.12 Derivation of the SOM Algorithm in the General Metric -- 3.13 What Kind of SOM Actually Ensues from the Distortion Measure? -- 3.14 Batch Computation of the SOM (“Batch Map”) -- 3.15 Further Speedup of SOM
Computation -- 4. Physiological Interpretation of SOM -- 4.1 Two Different Lateral Control Mechanisms -- 4.2 Learning Equation -- 4.3 System Models of SOM and Their Simulations -- 4.4 Recapitulation of the Features of the Physiological
SOM Model -- 5. Variants of SOM -- 5.1 Overview of Ideas to Modify the Basic SOM -- 5.2 Adaptive Tensorial Weights -- 5.3 Tree-Structured SOM in Searching -- 5.4 Different Definitions of the Neighborhood -- 5.5 Neighborhoods in the
Signal Space -- 5.6 Dynamical Elements Added to the SOM -- 5.7 Operator Maps -- 5.8 Supervised SOM -- 5.9 The Adaptive-Subspace SOM (ASSOM) -- 5.10 Feedback-Controlled Adaptive-Subspace SOM (FASSOM) -- 6. Learning Vector Quantization
-- 6.1 Optimal Decision -- 6.2 The LVQ1 -- 6.3 The Optimized-Learning-Rate LVQ1 (OLVQ1) -- 6.4 The LVQ2 (LVQ2.1) -- 6.5 The LVQ3 -- 6.6 Differences Between LVQ1, LVQ2 and LVQ3 -- 6.7 General Considerations -- 6.8 The Hypermap-Type LVQ
-- 6.9 The “LVQ-SOM” -- 7. Applications -- 7.1 Preprocessing of Optic Patterns -- 7.2 Acoustic Preprocessing -- 7.3 Process and Machine Monitoring -- 7.4 Diagnosis of Speech Voicing -- 7.5 Transcription of Continuous Speech -- 7.6
Texture Analysis -- 7.7 Contextual Maps -- 7.8 Organization of Large Document Files -- 7.9 Robot-Arm Control -- 7.10 Telecommunications -- 7.11 The SOM as an Estimator -- 8. Hardware for SOM -- 8.1 An Analog Classifier Circuit -- 8.2
Fast Digital Classifier Circuits -- 8.3 SIMD Implementation of SOM -- 8.4 Transputer Implementation of SOM -- 8.5 Systolic-Array Implementation of SOM -- 8.6 The COKOS Chip -- 8.7 The TInMANN Chip -- 9. An Overview of SOM Literature --
9.1 General -- 9.2 Early Works on Competitive Learning -- 9.3 Status of the Mathematical Analyses -- 9.4 Survey of General Aspects of the SOM -- 9.5 Modifications and Analyses of LVQ -- 9.6 Survey of Diverse Applications of SOM -- 9.7
Applications of LVQ -- 9.8 Survey of SOM and LVQ Implementations -- 9.9 New References in the Second Edition -- 10. Glossary of “Neural” Terms -- References
ISBN:9783642979668
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Springer Series in Information Sciences: 30
Keywords: Computer science , Neurosciences , Data mining , Artificial intelligence , Mathematics , Biophysics , Biological physics , Electrical engineering , Computer Science , Artificial Intelligence (incl. Robotics) , Data Mining and Knowledge Discovery , Neurosciences , Biophysics and Biological Physics , Communications Engineering, Networks , Mathematics, general
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Call number:SPRINGER-1997-9781461561231:ONLINE Show nearby items on shelf
Title:The Ordered Weighted Averaging Operators Theory and Applications
Author(s):
Date:1997
Size:1 online resource (347 p.)
Note:10.1007/978-1-4615-6123-1
Contents:1. Basic Issues in Aggregation -- Kolmogorov’ s theorem and its impact on soft computing -- Possibility and necessity in weighted aggregation -- OWA operators and an extension of the contrast model -- Equivalence of changes in
proportions at crossroads of mathematical theories -- 2. Fundamental Aspects of OWA Operators -- On the inclusion of importances in OWA aggregation -- On the linguistic OWA operator and extensions -- Alternative representations of OWA
operators -- 3. Mathematical Issues and OWA Operators -- Useful tools for aggregation procedures: some consequences and applications of Strossen ’s measurable Hahn-Banach theorem -- OWA specificity -- Ordered continuous means and
information -- 4. OWA Operators in Decision Analysis -- OWA operators in decision making with uncertainty and nonnumeric payoffs -- On the role of immediate probability in various decision making models -- Risk management using fuzzy
logic and genetic algorithms -- OWA operators for doctoral student selection problem -- 5. OWA Operators in Multicriteria and Multiperson Decision Making -- Beyond min aggregation in multicriteria decision: (ordered) weighted mean,
discri-min, leximin -- OWA operators in group decision making and consensus reaching under fuzzy preferences and fuzzy majority -- Applications of the linguistic OWA operators in group decision making -- Aggregation rules in committee
procedures -- 6. OWA Operators in Querying and Information Retrieval -- Quantified statements and some interpretations for the OWA operators -- Using OWA operators in flexible query processing -- Application of OWA operatrors to soften
information retrieval systems -- Implementation of OWA operators in fuzzy querying for Microsoft Access -- 7. OWA Operators in Learning and Classification -- OWA-based computing: learning algorithms -- OWA operators in machine learning
from imperfect examples -- An application of OWA operators to the aggregation of multiple classification decisions
ISBN:9781461561231
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Mathematics , Artificial intelligence , Management information systems , Computer science , Mathematical logic , Probabilities , Mathematics , Mathematical Logic and Foundations , Probability Theory and Stochastic Processes , Artificial Intelligence (incl. Robotics) , Management of Computing and Information Systems
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Call number:SPRINGER-1996-9783642997891:ONLINE Show nearby items on shelf
Title:Applied Mathematics and Parallel Computing Festschrift for Klaus Ritter
Author(s):
Date:1996
Size:1 online resource (376 p.)
Note:10.1007/978-3-642-99789-1
Contents:Informatics and the Internal Necessity for the Mathematization of the Sciences -- A New Semi-infinite Programming Method for Nonlinear Approximation -- Exhibition Organized by Klaus Ritter on the Occasion of the 125th Anniversary of
the Technical University of Munich -- Concavity of the Vector-Valued Functions Occuring in Fuzzy Multiobjective Decision-Making -- An Algorithm for the Solution of the Parametric Quadratic Programming Problem -- Optimal and
Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition -- Trust-Region Interior-Point Algorithms for Minimization Problems with Simple Bounds -- Adaptive Kernel Estimation of a Cusp-shaped Mode --
Automatic Differentiation: The Key Idea and an Illustrative Example -- An Approach to Parallelizing Isotonic Regression -- Mathematical Programming at Oberwolfach -- A SQP-Method for Linearly Constrained Maximum Likelihood Problems --
Machine Learning via Polyhedral Concave Minimization -- Optimization Concepts in Autonomous Mobile Platform Design -- A Fuzzy Set Approach for Optimal Positioning of a Mobile Robot Using Sonar Data -- Gradient Computation by Matrix
Multiplication -- Simulating Ultrasonic Range Sensors on a Transputer Workstation -- A Modular Architecture for Optimization Tutorials -- Differential Stability Conditions for Saddle Problems on Products of Convex Polyhedra --
Large-Scale Global Optimization on Transputer Networks -- The Statistical Art of Maximizing the Likelihood -- Remote Access to a Transputer Workstation -- An Extension of Multivariate Reliability Systems -- Automatic Differentiation: A
Structure-Exploiting Forward Mode with Almost Optimal Complexity for Kantorovi? Trees -- Approximate Structured Optimization by Cyclic Block-Coordinate Descent -- Author Index
ISBN:9783642997891
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Mathematics , Operations research , Decision making , Microprocessors , Computer science , Probabilities , Mathematics , Probability Theory and Stochastic Processes , Math Applications in Computer Science , Processor Architectures , Operation Research/Decision Theory
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Call number:SPRINGER-1995-9783642976100:ONLINE Show nearby items on shelf
Title:Self-Organizing Maps
Author(s): Teuvo Kohonen
Date:1995
Size:1 online resource (362 p.)
Note:10.1007/978-3-642-97610-0
Contents:1. Mathematical Preliminaries -- 1.1 Mathematical Concepts and Notations -- 1.2 Distance Measures for Patterns -- 1.3 Statistical Pattern Recognition -- 1.4 The Robbins-Monro Stochastic pproximation -- 1.5 The Subspace Methods of
Classification -- 1.6 Dynamically Expanding Context -- 2. Justification of Neural Modeling -- 2.1 Models, Paradigms, and Methods -- 2.2 On the Complexity of Biological Nervous Systems -- 2.3 Relation Between Biological and Artificial
Neural Networks -- 2.4 What Functions of the Brain Are Usually Modeled? -- 2.5 When Do We Have to Use Neural Computing? -- 2.6 Transformation, Relaxation, and Decoder -- 2.7 Categories of ANNs -- 2.8 Competitive-Learning Networks --
2.9 Three Phases of Development of Neural Models -- 2.10 A Simple Nonlinear Dynamic Model of the Neuron -- 2.11 Learning Laws -- 2.12 Brain Maps -- 3. The Basic SOM -- 3.1 The SOM Algorithm in the Euclidean Space -- 3.2 The
“Dot-Product SOM” -- 3.3 Preliminary Demonstrations of Topology-Preserving Mappings -- 3.4 Basic Mathematical Approaches to Self-Organization -- 3.5 Initialization of the SOM Algorithms -- 3.6 On the “Optimal” Learning-Rate Factor --
3.7 Effect of the Form of the Neighborhood Function -- 3.8 Magnification Factor -- 3.9 Practical Advice for the Construction of Good Maps -- 3.10 Examples of Data Analyses Implemented by the SOM -- 3.11 Using Gray Levels to Indicate
Clusters in the SOM -- 3.12 Derivation of the SOM Algorithm in the General Metric -- 3.13 What Kind of SOM Actually Ensues from the Distortion Measure? -- 3.14 Batch Computation of the SOM (“Batch Map”) -- 4. Physiological
Interpretation of SOM -- 4.1 Two Different Lateral Control Mechanisms -- 4.2 Learning Equation -- 4.3 System Models of SOM and Their Simulations -- 4.4 Recapitulation of the Features of the Physiological SOM Model -- 5. Variants of SOM
-- 5.1 Overview of Ideas to Modify the Basic SOM -- 5.2 Adaptive Tensorial Weights -- 5.3 Tree-Structured SOM in Searching -- 5.4 Different Definitions of the Neighborhood -- 5.5 Neighborhoods in the Signal Space -- 5.6 Dynamical
Elements Added to the SOM -- 5.7 Operator Maps -- 5.8 Supervised SOM -- 5.9 Adaptive-Subspace SOM (ASSOM) for the Implementation of Wavelets and Gabor Filters -- 5.10 Feedback-Controlled Adaptive-Subspace SOM (FASSOM) … -- 6. Learning
Vector Quantization -- 6.1 Optimal Decision -- 6.2 The LVQ1 -- 6.3 The Optimized-Learning-Rate LVQ1 (OLVQ1) -- 6.4 The LVQ2 (LVQ2.1) -- 6.5 The LVQ3 -- 6.6 Differences Between LVQ1, LVQ2 and LVQ3 -- 6.7 General Considerations -- 6.8
The Hypermap-Type LVQ -- 6.9 The “LVQ-SOM” -- 7. Applications -- 7.1 Preprocessing -- 7.2 Process and Machine State Monitoring -- 7.3 Diagnosis of Speech Voicing -- 7.4 Transcription of Continuous Speech -- 7.5 Texture Analysis -- 7.6
Contextual Maps -- 7.7 Robot-Arm Control I -- 7.8 Robot-Arm Control II -- 8. Hardware for SOM -- 8.1 An Analog Classifier Circuit -- 8.2 A Fast Digital Classifier Circuit -- 8.3 SIMD Implementation of SOM -- 8.4 Transputer
Implementation of SOM -- 8.5 Systolic-Array Implementation of SOM -- 8.6 The COKOS Chip -- 8.7 The TInMANN Chip -- 9. An Overview of SOM Literature -- 9.1 General -- 9.2 Early Works on Competitive Learning -- 9.3 Status of the
Mathematical Analyses -- 9.4 Survey of General Aspects of the SOM -- 9.5 Modifications and Analyses of LVQ -- 9.6 Survey of Diverse Applications of SOM -- 9.7 Applications of LVQ -- 9.8 Survey of SOM and LVQ Implementations -- 10.
Glossary of “Neural” Terms -- References
ISBN:9783642976100
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Springer Series in Information Sciences: 30
Keywords: Physics , Mathematics , Biophysics , Biological physics , Electrical engineering , Physics , Biophysics and Biological Physics , Communications Engineering, Networks , Mathematics, general
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Call number:SPRINGER-1994-9783642468087:ONLINE Show nearby items on shelf
Title:Information Systems and Data Analysis Prospects — Foundations — Applications
Author(s):
Date:1994
Size:1 online resource (463 p.)
Note:10.1007/978-3-642-46808-7
Contents:I: Information Processing, Classification-Based Approaches, Linguistic and Conceptual Analysis -- Learning and Case-Based Reasoning -- Induction and Case-Based Reasoning for Classification Tasks -- Symbolic Learning and
Nearest-Neighbor Classification -- Nonstandard Concepts of Similarity in Case-Based Reasoning -- Learning from Cases for Classification Problem Solving -- Approaches Based on Linguistic Analysis -- Methods of Phoneticizing in Regard to
Spelling Variants of Medical Phrases -- Disambiguating Lexical Meaning: Conceptual meta-modelling as a means of controlling semantic language analysis -- Information Retrieval Using Conceptual Representations of Phrases -- Information
Processing and Design of Information Systems -- Communication in Distributed Heterogenous Systems -- Prerequisites and Development Perspectives for Information Processing in the Social Sciences -- Aspects of Coupling Logic Programming
and Databases -- Processing Scientific Networks in Bibliographic Databases -- Object-Oriented Systems Analysis Applied to the Method Component of a Knowledge-Based System for Data Analysis -- Classification Based Query Evaluation in
Knowledge Base Systems -- Uncertainity and Neural Networks -- Neural Networks: Architectures, Learning and Performance -- Reasoning with Uncertainty in Diagnostic Systems -- Conceptual Approaches -- Der klassische und der moderne
Begriff des Begriffs. Gedanken zur Geschichte der Begriffsbildung in den exakten Wissenschaften -- Ideas of Algebraic Concept Analysis -- Conceptual Structures in Mathematical Logic and Their Formal Representation -- II: Mathematical
and Statistical Methods for Classification and Data Analysis -- Clustering and Discrimination -- Three-Mode Hierarchical Cluster Analysis of Three-Way Three-Mode Data -- The Testing of Data Structures with Graph-Theoretical Models --
Geometric Approach to Evaluating Probabilities of Correct Classification into two Gaussian or Spherical Categories -- Jackknife Estimates of Similarity Coefficients Obtained from Quadrat Sampling of Species -- Clustering Techniques in
the Computing Environment XploRe -- On the Application of Discriminant Analysis in Medical Diagnostics -- Similarity Searching in Databases of Three-Dimensional Chemical Structures -- Feature Generation and Classification of Time
Series -- Data Analysis Methods -- TRIPAT: a Model for Analyzing Three-Mode Binary Data -- Block-relaxation Algorithms in Statistics -- Multidimensional Scaling with lp-Distances, a Unifying Approach -- Universal Optimality of Rank
Constrained Matrix Approximation -- The Analysis of Spatial Data from Marine Ecosystems -- Automatic Decomposition of Lattice Data including Missing Values and Boundaries -- III: Genome and Molecular Sequence Analysis -- Classification
and Data Analysis in Genome Projects: Some Aspects of Mapping, Alignment and Tree Reconstruction -- Multiple Alignment of Protein Sequences and Construction of Evolutionary Trees based on Amino Acid Properties — an Algebraic Approach
-- How to Deal With Third Codon Positions in Phylogenetic Analysis -- Machine Learning for Protein Structure Prediction -- A Parallel-Processor Implementation of an Algorithm to Delineate Distantly Related Protein Sequences using
Conserved Motifs and Neural Networks -- IV: Applied Data Analysis in Special Fields -- Economy and Marketing -- Knowledge-Based Selection and Application of Quantitative Models of Consumer Behavior -- Goodwill towards Domestic Products
as Segmentation Criterion: An Empirical Study within the Scope of Research on Country-of-Origin Effects -- Archeology -- Merovingian Glass Beads: An Essay of Classification -- Screening in Medicine -- Statistical Measures to Quantify
the Benefit from Screening: a Case Study on Cholesterol Screening -- Evaluation of Screening in Case-Control Studies: an Alternative to Randomized Controlled Trials? -- List of Authors
ISBN:9783642468087
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Health informatics , Computer science , Bioinformatics , Computational biology , Probabilities , Statistics , Economic theory , Economics , Economic Theory/Quantitative Economics/Mathematical Methods , Mathematics of Computing , Probability Theory and Stochastic Processes , Statistics for Business/Economics/Mathematical Finance/Insurance , Health Informatics , Computer Appl. in Life Sciences
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Call number:SPRINGER-1992-9781461392293:ONLINE Show nearby items on shelf
Title:Modeling Complex Phenomena Proceedings of the Third Woodward Conference, San Jose State University, April 12–13, 1991
Author(s):
Date:1992
Size:1 online resource (313 p.)
Note:10.1007/978-1-4613-9229-3
Contents:A Complex Phenomenon: The Human Mind -- I Paradigms, Complexity, and Learning -- Modeling and Control of Complex Systems: Paradigms and Applications -- Knowledge and Meaning: Chaos and Complexity -- Complexity Issues in Robotic
Machine Learning of Natural Languages -- II Forecasting and Arms Race -- Nonlinear Forecasting, Chaos and Statistics -- Nonlinear Dynamics and Chaos in Arms Race Models -- III Economic Systems -- Chaotic Dynamics in Economic
Equilibrium Theory -- Chaos and the Foreign Exchange Market -- IV Earthquakes and Sandpiles -- Earthquakes as a Complex Phenomenon -- Application of a Mean Field Approximation to Two Systems that Exhibit Self-Organized Criticality -- V
Fluids and Crystal Growths -- Modeling the Hydrodynamics of Materials Processing -- Modeling Complex Phenomena in Fluids -- VI Complex Patterns -- Consensus in Small and Large Audiences -- Nonhomogeneous Response of Reaction-Diffusion
Systems to Local Perturbations -- Nonequilibrium Transient Near a Noise-Induced Instability -- Active Walker Models for Filamentary Growth Patterns -- Index of Contributors
ISBN:9781461392293
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Physics , Physical chemistry , Mechanics , Statistical physics , Dynamical systems , Complexity, Computational , Physics , Statistical Physics, Dynamical Systems and Complexity , Complexity , Mechanics , Physical Chemistry
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Call number:SPRINGER-1989-9783642839573:ONLINE Show nearby items on shelf
Title:Advanced Robotics: 1989 Proceedings of the 4th International Conference on Advanced Robotics Columbus, Ohio, June 13–15, 1989
Author(s):
Date:1989
Size:1 online resource (687 p.)
Note:10.1007/978-3-642-83957-3
Contents:A-1: Hand Design -- On a New Torque Sensor for Compliant Grasp by Robot Fingers with a Tendon Actuation System -- A Learning Mechanism of Control Parameters for The Variable Structure Fuzzy Logic Controller -- B-1: Manipulator
Kinematics (I) -- On the Inverse Kinematics of Redundant Manipulators: Characterization of the Self-Motion Manifolds -- Symbolic Equation Generation for Manipulators -- An Approach to Manipulator Kinematic Modeling -- A-2: Mobile
Robots -- Fundamental Studies of Collision Avoidance Problems Between a Robot and a Moving Obstacle. Part 1 Heuristic Collision Avoidance Techniques and Fundamental Experiments with a Real Robot -- Model and Sensor Based Precise
Navigation by an Autonomous Mobile Robot -- Reactive Processes for Mobile Robot Control -- Powered Wheelchair Equipped with Voice Control and Automatic Locomotion -- B-2: Flexible Arm Control -- Nonlinear Decoupling Control/Control of
Flexible Robots -- Application and Comparison of On-Line Identification Methods for Flexible Manipulator Control -- Trajectory Shaping for Flexible Manipulators: A Comparative Study -- A-3: Manipulator Dynamics -- An Algorithm for
Efficient Computation of Dynamics of Robotic Manipulators -- Calculation of the Minimum Inertial Parameters of Tree Structure Robots -- Parallel Algorithms and Architectures for Manipulator Inverse Dynamics -- B-3: Actuators and
Sensors -- Shape Memory Alloy Actuators for Robotic End-Effectors -- An Artificial Muscle Actuator for Robots -- High Precision Mark Position Sensing Device R-HPSD Suitable for 3-D Position Determination in Robotics -- The Ultrasonic
Inspection Robot System -- A-4: Multiple-Limb Co-ordination and Legged Locomotion -- An Approach to Simultaneous Control of Trajectory and Interaction forces in Dual Arm Configurations -- Realization of Dynamic Biped Walking Walking
Stabilized With Trunk Motion Under Known External Force -- Power System of a Multi-Legged Walking Vehicle -- On the Stability Properties of 2N-Legged Wave Gaits -- B-4: Vision -- Depth Perception for Robots: Structural Stereo from
Extended Laplacian-of-Gaussian Features -- Location of the Mobile Robot CENTAURE in a Modelled Environment with PYRAMIDE -- An Auto-Calibration System for Vision-Servoed Robots -- Visual Servoing of a Robot Assembly Task -- A-5: Path
Planning and Collision Avoidance -- Computing Moveability Areas of a Robot Among Obstacles Using Octrees -- Determination of the Space Occupied by Moving Links of Manipulators and Linkages -- Collision-Free Path Planning for a
Reconfigurable Robot -- Characterizing Repetition in Workcell Applications and the Implications for Sequence Optimization -- B-5: Hybrid Control (I) -- Analysis and Design of a Six Axis Truss Type of Force Sensor -- The Interaction of
Robots with Passive Environments: Application to Force Feedback Control -- A-6: Manipulator Kinematics (II) -- The Dynamic Model of a Three Degree of Freedom Parallel Robotic Shoulder Module -- Kinematics of Redundantly Actuated Closed
Kinematic Chains -- Identifying the Kinematics of Non-Redundant Serial Chain Manipulators by a Closed-loop Approach -- General Dynamic Formulation of the Force Distribution Equations -- B-6: Cooperative Manipulation -- An Overview of
KALI: a System to Program and Control Cooperative Manipulators -- A-7: Hybrid Control (II) -- A Local Solution with Global Characteristics for the Joint Torque Optimization of a Redundant Manipulator -- Joint Compliance Control of the
Anthropomorphic Manipulator -- Independent Joint Controllability of Manipulator System -- A Generalized Approach for the Control of Constrained Robots -- B-7: Legged Locomotion and Adaptive Vehicles -- A Study on Walking Robots
Controlled with Attitude Sensors -- Development on Walking Robot for Underwater Inspection -- Locomotion of a Machine of a Static Crawler Type: Gait Modelling -- Terrain Adaptive Tracked Vehicle HELIOS-I
ISBN:9783642839573
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Physics , Mechanics , Engineering design , Control engineering , Robotics , Mechatronics , Engineering economics , Engineering economy , Manufacturing industries , Machines , Tools , Physics , Mechanics , Engineering Design , Manufacturing, Machines, Tools , Control, Robotics, Mechatronics , Engineering Economics, Organization, Logistics, Marketing
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Call number:SPRINGER-1978-9781475701753:ONLINE Show nearby items on shelf
Title:Evaluating New Telecommunications Services
Author(s):
Date:1978
Size:1 online resource (784p p.)
Note:10.1007/978-1-4757-0175-3
Contents:Section 1 Overview of Research Issues -- User Research and Demand Research: What’s the Use? -- Technology and Structures — Man and Machine -- The Role of Telecommunications Policy Analysis in Service Planning -- Communications Policy
— The Need for Research -- Section 2 Public Services: The Delivery of Health Care -- The “Patient Trajectory”: A Modeling Tool for Planning and Evaluating Rural Telemedicine Systems -- Telehealth Care in Canada -- A Methodology for
Design of Advanced Technology-based Health Care Systems in Developing Countries -- Section 3 Public Services: Education and Community -- Educational Experiments with the Communications Technology Satellite: A Memo from Evaluators to
Planners -- Evaluations of Interactive Tele-education in the Public Service Commission -- Open Choice — New Communication Systems and Applications at the British Open University -- Serial Experimentation for the Management and
Evaluation of Communications Systems -- The Development of Two-way Cable Television: Applications for the Community -- Beyond Statistics -- Section 4 Information Services -- The Impact of Telecommunications Technologies on Informal
Communication in Science and Engineering — Research Needs and Opportunities -- Scientific Communication and Knowledge Representation -- Communications Aspects of Euronet -- Problems in Forecasting the Price and Demand for On-Line
Information Services -- The Economics and Cost Benefit of Analysis Services — The Case of Information Analysis Centers -- Technology Assessment and Idealized Design -- Section 5 Teleconferencing and Computer Conferencing -- Use and
Traffic Characteristics of Teleconferencing for Business -- Evaluation of the Potential Market for Various Future Communication Modes via an Analysis of Communication Flow Characteristics -- Learning the Limits of Teleconferencing:
Design of a Teleconference Tutorial -- Interpersonal Teleconferencing in an Organizational Context -- Organizational Communication Behavior: Description and Prediction -- Measuring the Dimensions of Interpersonal Communication --
Computer Assisted Communication in a Directorate of the Canadian Federal Government — A Pilot Study -- Exploiting the Tele- in Teleconferencing -- Section 6 New Services -- Bell System Visual Communications Systems -- The Swedish
Market for a Public Switched Multi-Purpose Broadband Network -- A Possible European System for Teleconferencing via Satellite -- Viewdata Networks -- Computerized Conferencing: A Review and Statement of Issues -- Section 7 The
Information Society -- Information Technology and Society -- Information and Communication: Is There A System? -- Information, Energy and Labour Force -- Electronic Funds Transfer in Perspective -- Section 8 Design and Planning -- The
Design of the Designing Community -- The Utility of Social Experimentation in Policy Research -- New Telecommunications Services and Regional Development: Approaches to Experimentation and Planning -- Planning Exploratory Trials of New
Interpersonal Telecommunications -- Concluding Discussion -- Policy -- Methodology
ISBN:9781475701753
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:Nato Conference Series : 6
Keywords: Physics , Physics , Physics, general
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Call number:SPRINGER-1978-9781461590774:ONLINE Show nearby items on shelf
Title:Language Interpretation and Communication
Author(s):
Date:1978
Size:1 online resource (428 p.)
Note:10.1007/978-1-4615-9077-4
Contents:Section 1. Conference Interpretation — an Introduction -- 1. Language Interpretation and Communication: Introduction to the Proceedings -- 2. How Conference Interpretation Grew -- 3. Selection and Training of Conference Interpreters
-- 4. Reflections on the Training of Simultaneous Interpreters: A metalinguistic approach -- 5. Intercultural Communication and the Training of Interpreters at the Monterey Institute of Foreign Studies -- 6. An Integrated Programme for
Training Interpreters -- Section 2. Sign Language and Sign Language Interpretation -- 7. The Role of Oral Language in the Evolution of Manual Language -- 8. Sign Language Interpretation: The State of the Art -- 9. Research in Sign
Language Interpreting at California State University, Northridge -- 10. Sign Language and Psycholinguistic Process: Fact, Hypotheses and Implications for Interpretation -- 11. Sign Language Interpretation and General Theories of
Language, Interpretation and Communication -- Section 3. Bilingualism, Translation and Interpretation -- 12. Linguistic Abilities in Translators and Interpreters -- 13. Psychological Approaches to Bilingualism, Translation and
Interpretation -- 14. True Bilingualism and Second Language Learning -- 15. Translating as an Innate Skill -- 16. Four Generations of Machine Translation Research and Prospects for the Future -- Section 4. Linguistic, Sociolinguistic
and Social Approaches -- 17 On the Distinction between Linguistics and Pragmatics -- 18. Language Meaning and Message Meaning: Towards a Sociolinguistic Approach to Translation -- 19. Contributions of Cross-Cultural Orientation
Programs and Power Analysis to Translation/Interpretation -- 20. Interpreter Roles and Interpretation Situations: Cross-Cutting Typologies -- 21. Behavioral Aspects of Liaison Interpreters in Papua New Guinea: Some Preliminary
Observations -- Section 5. Psychological Approaches -- 22. On the Representations of Experience -- 23. The Bilingual’s Performance: Language Dominance, Stress, and Individual Differences -- 24. Summary and Recall of Text in First and
Second Languages: Some Factors Contributing to Performance Differences -- 25. Psychosemantics and Simultaneous Interpretation -- 26. An Information-Processing Model of Understanding Speech -- Section 6. Theory and Research in
Conference Interpretation -- 27. Human Factors Approach to Simultaneous Interpretation -- 28. Simultaneous Interpretation — Units of Meaning and Other Features -- 29. Language and Cognition -- 30. Syntactic Anticipation in
German-English Simultaneous Interpreting -- 31. Simultaneous Interpretation: A Hypothetical Model and its Practical Application -- 32. Adult Simultaneous Interpretation: A Functional Analysis of Linguistic Categories and a Comparison
with Child Development -- Section 7. Conclusion -- 33. The Contribution of Cognitive Psychology to the Study of Interpretation -- Appendix A. Discussion Report -- Appendix B. List of Participants -- Name Index
ISBN:9781461590774
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Series:NATO Conference Series : 6
Keywords: Linguistics , Applied linguistics , Linguistics , Applied Linguistics
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Call number:Q387.D66::2015 Show nearby items on shelf
Title:The master algorithm: How the quest for the ultimate learning machine will remake our world
Author(s): Pedro Domingos
Date:2015
Publisher:Basic Books
Size:352 p.
ISBN:9780465065707
Keywords: Knowledge representation (Information theory) , Artificial intelligence Social aspects. , Artificial intelligence Philosophy. , Cognitive science Mathematics. , Algorithms.
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Call number:Q327.D83::2001 Show nearby items on shelf
Title:Pattern classification
Author(s): Richard O. Duda
Peter E. Hart
David G. Stork
Date:2001
Publisher:New York : John Wiley & Sons
Note:2nd ed.
Note:654 p.
Contents:Bayesian Decision Theory. Maximum-Likelihood and Bayesian Parameter Estimation. Nonparametric Techniques. Linear Discriminant Functions. Multilayer Neural Networks. Stochastic Methods. Nonmetric Methods. Algorithm-Independent Machine Learning. Unsupervised Learning and Clustering
ISBN:0471056693
Keywords: Pattern recognition systems , Statistical decision , Pattern Recognition , Statistics , Perceptrons
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Call number:Q327.B52::2006 Show nearby items on shelf
Title:Pattern Recognition and Machine Learning
Author(s): Christopher M. Bishop
Date:2006
Publisher:Springer
ISBN:0387310738
Series:Information Science and Statistics
Keywords: Pattern Perception , Machine Learning
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Call number:Q325.5::2015 Show nearby items on shelf
Title:Machine learning in python: Essential techniques for predictive analysis
Author(s): Michael Bowles
Date:2015
Publisher:Indianapolis, IN: John Wiley & Sons
Size:326 p.
Contents:The two essential algorithms for making predictions -- Understand the problem by understanding the data -- Predictive model building : balancing performance, complexity, and big data -- Penalized linear regression -- Building predictive models using p enalized linear methods -- Ensemble methods -- Building ensemble models with Python.
ISBN:9781118961742
Keywords: Machine learning , Python (Computer program language)
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Call number:Q325.5.W67::1991 Show nearby items on shelf
Title:Proceedings of the Fourth Annual Workshop on Computational Learning Theory, University of California, Santa Cruz, August 5-7, 1991
Conference:Workshop on Computational Learning Theory, University of California, Santa Cruz, 1991
Author(s): Manfred Warmuth
Date:1991
Publisher:M. Kaufmann Publishers, San Mateo, Calif
Size:383
ISBN:1558602135
Keywords: Machine learning Congresses. , Conference proceedings , Conferences
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Call number:Q325.5.K455::2015 Show nearby items on shelf
Title:Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies
Author(s): John D. Kelleher
Brian Mac Namee
Aoife D'Arcy
Size:595 p.
Contents:Machine learning for predictive data analytics -- Data to insights to decisions -- Data exploration -- Information-based learning -- Similarity-based learning -- Probability-based learning -- Error-based learning -- Evaluation -- Case study : customer churn -- Case study : galaxy classification -- The art of machine learning for predictive data analytics.
ISBN:9780262029445
Keywords: Machine learning. , Data mining. , Prediction theory.
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Call number:Q325.5.H39::2009 Show nearby items on shelf
Title:The elements of statistical learning Data mining, inference, and prediction
Author(s): Trevor Hastie
Robert Tibshirani
Jerome H. Friedman
Date:2009
Edition:2nd ed.
Publisher:New York : Springer
Size:745 p
Contents:Introduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Ad ditive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning -- -- Random forests -- -- Ensemble learning - - -- Undirected graphical models -- -- High-dimensional problems
ISBN:9780387848587
Series:Springer series in statistics
Keywords: Machine learning. , Statistics Methodology. , Data mining. , Bioinformatics. , Inference. , Forecasting. , Computational intelligence.
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Call number:Q325.5.G66::2016 Show nearby items on shelf
Title:Deep learning
Author(s): Yoshua Bengio Ian Goodfellow and Aaron Courville
Date:2016
Publisher:Cambridge, Massachusetts : The MIT Press
Size:775 p
Contents:Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- O ptimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Struct ured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models
ISBN:9780262035613
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Call number:Q325.5.C75::2000 Show nearby items on shelf
Title:An introduction to support vector machines : and other kernel-based learning methods
Author(s): Nello Cristianini
John Shawe-Taylor
Date:2000
Publisher:Cambridge University Press
ISBN:0521780195
Keywords: Machine learning , Algorithms , Kernel functions , Data mining
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Call number:Q325.5.A28::2012 Show nearby items on shelf
Title:Learning from data: a short course
Author(s): Yaser S. Abu-Mostafa
Malik Magdon-Ismail
Hsuan-Tien Lin
Date:2012
Publisher:[United States]: AMLBook.com
Size:201 p
Contents:1. The learning problem -- 2. Training versus testing -- 3. The linear model -- 4. Overfitting -- 5. Three learning principles -- Epilogue -- Further reading -- Appendix
ISBN:9781600490064
Keywords: Machine learning, Textbooks.
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Call number:QC793.47.S83.N37::2014 Show nearby items on shelf
Title:Statistical analysis techniques in particle physics: fits, density estimation and supervised learning
Author(s): Ilya. Narsky
Frank Clifford Porter
Date:2013
Publisher:Weinheim: Wiley-VCH
Size:459 p
Contents:Why We Wrote This Book and How You Should Read It -- Parametric Likelihood Fits -- Goodness of Fit -- Resampling Techniques -- Density Estimation -- Basic Concepts and Definitions of Machine Learning -- Data Preprocessing -- Linear Transformations a nd Dimensionality Reduction -- Introduction to Classification -- Assessing Classifier Performance -- Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression -- Neural Networks -- Local Learning and Kernel Exp a nsion -- Decision Trees -- Ensemble Learning -- Reducing Multiclass to Binary -- How to Choose the Right Classifier for Your Analysis and Apply It Correctly -- Methods for Variable Ranking and Selection -- Bump Hunting in Multivariate Data -- Software P ac kages for Machine Learning -- Appendix A: Optimization Algorithms.
ISBN:9783527410866
Keywords: Particles (Nuclear physics), Statistical methods
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Call number:QB51.3.E43::2014 Show nearby items on shelf
Title:Statistics, data mining, and machine learning in astronomy A practical python guide for the analysis of survey data
Author(s): Zeljko Ivezic
Andrew Connolly
Jacob T VanderPlas
Alexander Gray
Date:2014
Publisher:Princeton, N.J. : Princeton University Press
Size:540 p.
ISBN:9780691151687
Series:Princeton series in modern observational astronomy
Keywords: Astronomy, Data processing , Statistical astronomy
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Call number:QA76.9.D343::2017 Show nearby items on shelf
Title:An Introduction to Machine Learning
Author(s): Miroslav Kubat
Date:2017
ISBN:9783319639123
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Location: SUGGESTIONS (email library@fnal.gov if you would like this title added to the Library collection.)

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Call number:QA76.73.P98I3::2014 Show nearby items on shelf
Title:Python data analysis: learn how to apply powerful data analysis techniques with popular open source Python modules
Author(s): Ivan Idris
Date:2014
Publisher:Birmingham, UK: Packt Publishing Ltd
Size:329 p
Contents:Getting started with Python libraries -- NumPy arrays -- Statistics and linear algebra -- pandas primer -- Retrieving, processing, and storing data -- Data visualization -- Signal processing and time series -- Working with databases -- Analyzing tex tual data and social media -- Predictive analytics and machine learning -- Environments outside the Python ecosystem and cloud computing -- Performance tuning, profiling, and concurrency -- Appendix A : key concepts -- Appendix B : useful functions -- App endix C : online resources.
ISBN:9781783553358
Keywords: Python (Computer program language) , Programming languages (Electronic computers)
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Call number:QA76.73.P98G78::2015 Show nearby items on shelf
Title:Data science from scratch: First principles with Python
Author(s): Joel Grus
Date:2015
Publisher:O'Reilly Media
Size:311 p.
Contents:Introduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Go forth and do dat a science.
ISBN:9781491901427
Keywords: Python (Computer program language) , Database management. , Data structures (Computer science)
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Call number:QA76.73.P98C32::2018 Show nearby items on shelf
Title:Deep learning with Python
Author(s): François Chollet
Date:2018
Publisher:Manning Publications Co.
Size:361 p
Contents:What is deep learning? -- Before we begin: the mathematical building blocks o fneural networks -- Getting started with neural networks -- Fundamentals of machine learning -- Deep learning for computer vision -- Deep learning for text and sequences -- Advanced deep-learning best practices -- Generative deep learning -- Conclusions
ISBN:9781617294433
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Call number:QA325.5.P38::2017 Show nearby items on shelf
Title:Deep learning : a practitioner's approach
Author(s): Josh AUTHOR = Gibson Patterson Adam
Date:2017
Edition:First edition
Publisher:Sebastopol, CA : O'Reilly
Size:507 p
Contents:A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architectures of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on spark
ISBN:9781491914250
Keywords: Machine learning , Neural networks , Open source software
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Call number:QA325.5.B837::2017 Show nearby items on shelf
Title:Fundamentals of deep learning : designing next-generation machine intelligence algorithms
Author(s): Nikhil Buduma
Date:2017
Edition:First Edition
Publisher:O'Reilly Media
Size:283 p
Contents:The neural network -- Training feed-forward neural networks -- Implementing neural networks in TensorFlow -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Memory augmented neural networks -- Deep reinforcement learning
ISBN:9781491925614
Keywords: Deep Learning , Neural networks , Machine learning , Artificial intelligence
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Location: NEW

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