A 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.

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