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A Probabilistic Theory of Pattern Recognition 1996. Corr. 2nd Edition
Contributor(s): Devroye, Luc (Author), Györfi, Laszlo (Author), Lugosi, Gabor (Author)
ISBN: 0387946187     ISBN-13: 9780387946184
Publisher: Springer
OUR PRICE:   $132.99  
Product Type: Hardcover - Other Formats
Published: April 1996
Qty:
Annotation: 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.
Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - General
Dewey: 003.520
LCCN: 95044633
Series: Stochastic Modelling and Applied Probability
Physical Information: 1.6" H x 6.49" W x 9.55" (2.43 lbs) 638 pages
 
Descriptions, Reviews, Etc.
Publisher 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.