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Stochastic Modelling and Applied Probability Ser.: Probabilistic Theory of Pattern Recognition by László Györfi, Gabor Lugosi and Luc Devroye (1996, Hardcover)
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About this product
Product Identifiers
PublisherSpringer New York
ISBN-100387946187
ISBN-139780387946184
eBay Product ID (ePID)203797
Product Key Features
Number of PagesXv, 638 Pages
Publication NameProbabilistic Theory of Pattern Recognition
LanguageEnglish
Publication Year1996
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.
FormatHardcover
Dimensions
Item Height0.6 in
Item Weight85.4 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN95-044633
Dewey Edition20
TitleLeadingA
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
SynopsisA self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field., Pattern 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
