A Probabilistic Theory of Pattern Recognition

Luc Devroye, Laszlo Györfi, Gabor Lugosi

  • 出版商: Springer
  • 出版日期: 1997-02-20
  • 售價: $5,076
  • 貴賓價: 9.5$4,822
  • 語言: 英文
  • 頁數: 638
  • 裝訂: Hardcover
  • ISBN: 0387946187
  • ISBN-13: 9780387946184

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 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.

Table of 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
* Appendix
* Notation
* References
* Index