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A Probabilistic Theory of Pattern Recognition
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A Probabilistic Theory of Pattern Recognition

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Luc Devroye
1129 g
244x166x45 mm

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.
Preface Introduction The Bayes Error Inequalities and alternatedistance measures Linear discrimination Nearest neighbor rules Consistency Slow rates of convergence Error estimation The regularhistogram rule Kernel rules Consistency of the k-nearest neighborrule 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 resubstitutionestimate Deleted estimates of the error probability Automatickernel rules Automatic nearest neighbor rules Hypercubes anddiscrete spaces Epsilon entropy and totally bounded sets Uniformlaws of large numbers Neural networks Other error estimates Feature extraction Appendix Notation References Index
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.

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