A Probabilistic Theory of Pattern Recognition

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