A probabilistic theory of pattern recognition (Book, 1996) [WorldCat.org]
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A probabilistic theory of pattern recognition

Author: Luc Devroye; László Györfi; Gábor Lugosi
Publisher: New York : Springer, ©1996.
Series: Applications of mathematics, 31.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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,
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Details

Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Luc Devroye; László Györfi; Gábor Lugosi
ISBN: 0387946187 9780387946184
OCLC Number: 33276839
Description: xv, 636 pages : illustrations ; 24 cm
Contents: 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.
Series Title: Applications of mathematics, 31.
Responsibility: Luc Devroye, László Györfi, Gábor Lugosi.
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Abstract:

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and  Read more...

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