A Probabilistic Theory of Pattern Recognition

by Luc Devroye, László Györfi, Gábor Lugosi

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.

Genres: Textbooks, Mathematics

653 pages, Hardcover

First published February 20, 1996

Reviews

[Richard Zhu · Rating 4 out of 5]

> Pattern recognition is thus easier than regression function estimation.

Worth reading for this section alone