A Probabilistic Theory of Pattern Recognition [electronic resource] / by Luc Devroye, László Györfi, Gábor Lugosi
- Devroye, Luc
- New York, NY : Springer New York : Imprint: Springer, 1996.
- Physical Description:
- XV, 638 pages : online resource
- Additional Creators:
- Györfi, László, Lugosi, Gábor, and SpringerLink (Online service)
- Stochastic Modelling and Applied Probability, 0172-4568 ; 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.
- Digital File Characteristics:
- text file PDF
- AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
View MARC record | catkey: 15201621