A Probabilistic Theory of Pattern Recognition: 31 (Stochastic Modelling and Applied Probability, 31) - Hardcover

Devroye, Luc; Györfi, Laszlo; Lugosi, Gabor

Hardcover

ISBN 10: 0387946187 ISBN 13: 9780387946184

Publisher: Springer, 1996

Synopsis

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.

About this title

From the Back Cover

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, tree 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.

Edition details

• Publisher: Springer
• Publication date: 1996
• Language: English
• ISBN 10: 0387946187
• ISBN 13: 9780387946184
• Binding: Hardcover
• Number of pages: 653