A Probabilistic Theory of Pattern Recognition

Description

 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

Preface

* 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