A Probabilistic Theory of Pattern Recognition
Publisher: Springer
Published: Nov 22, 2013
Format: paperback
Number of pages: 653
ISBN-13: 9781461268772
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.
Notes: Source title: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability (31))