A Probabilistic Theory of Pattern Recognition (Hardcover, 1996. Corr. 2nd)

Preface Introduction The Bayes Error Inequalities and alternatedistance measures Linear discrimination Nearest neighbor rules Consistency Slow rates of convergence Error estimation The regularhistogram rule Kernel rules Consistency of the k-nearest neighborrule 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 resubstitutionestimate Deleted estimates of the error probability Automatickernel rules Automatic nearest neighbor rules Hypercubes anddiscrete spaces Epsilon entropy and totally bounded sets Uniformlaws of large numbers Neural networks Other error estimates Feature extraction Appendix Notation References Index