ENGLISH

A Probabilistic Theory of Pattern Recognition

By Luc Devroye , Laszlo Gyorfi , Gábor Lugosi (auth.)

Book information

Publisher
Springer-Verlag New York
Year
1996
ISBN
978-1-4612-6877-2, 978-1-4612-0711-5
DOI
10.1007/978-1-4612-0711-5
Language
english
Format
PDF
Filesize
26 MB (27269993 bytes)
Series
Stochastic Modelling and Applied Probability 31
Edition
1
Pages
638\631
Time added
2014-01-18 08:00:00
Probability Theory and Stochastic Processes; Patte

Description

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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