Probabilistic Theory of Pattern Recognition - 31

Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed.

The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches.

The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks.

Wherever possible, distribution-free properties and inequalities are derived.

A substantial portion of the results or the analysis is new.

Over 430 problems and exercises complement the material.