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Stochastic Modelling and Applied Probability Ser.: Probabilistic Theory of Pattern Recognition by László Györfi, Gabor Lugosi and Luc Devroye (2013, Trade Paperback)
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About this product
Product Identifiers
PublisherSpringer New York
ISBN-10146126877X
ISBN-139781461268772
eBay Product ID (ePID)16038369519
Product Key Features
Number of PagesXv, 638 Pages
LanguageEnglish
Publication NameProbabilistic Theory of Pattern Recognition
Publication Year2013
SubjectProbability & Statistics / General, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
TypeTextbook
Subject AreaMathematics, Computers, Psychology
AuthorLászló Györfi, Gabor Lugosi, Luc Devroye
SeriesStochastic Modelling and Applied Probability Ser.
FormatTrade Paperback
Dimensions
Item Weight35.2 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
TitleLeadingA
Dewey Edition20
Series Volume Number31
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal003/.52/015192
Table Of ContentPreface * Introduction * The Bayes Error * Inequalities and alternate distance measures * Linear discrimination * Nearest neighbor rules * Consistency * Slow rates of convergence Error estimation * The regular histogram rule * Kernel rules Consistency of the k-nearest neighbor rule * Vapnik-Chervonenkis theory * Combinatorial aspects of Vapnik- Chervonenkis theory * Lower bounds for empirical classifier selection * The maximum likelihood principle * Parametric classification * Generalized linear discrimination * Complexity regularization * Condensed and edited nearest neighbor rules * Tree classifiers * Data- dependent partitioning * Splitting the data * The resubstitution estimate * Deleted estimates of the error probability * Automatic kernel rules * Automatic nearest neighbor rules * Hypercubes and discrete spaces * Epsilon entropy and totally bounded sets * Uniform laws of large numbers * Neural networks * Other error estimates * Feature extraction * Appendix * Notation * References * Index
SynopsisPattern recognition presents a significant challege for scientists and engineers, and many different approaches have been proposed. This book provides a self-contained account of probabilistic techniques that have been applied to the subject. Researchers and graduate students will benefit from this wide-ranging account of the field., Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material.
LC Classification NumberQA273.A1-274.9
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