Title: A probabilistic theory of pattern recognition
First published: 1996
Description: 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.
Subjects: Probabilities, Pattern perception, Mathematics, Distribution (Probability theory), Optical pattern recognition, Probability Theory and Stochastic Processes
LC classification: Q327 .D5 1996