A probabilistic theory of pattern recognition
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Author
Contributions
- Györfi, László. - Contributor
- Lugosi, Gábor. - Contributor
Publication
1996 - Springer, New York, New York (State)
Language
English
Word Count
159,000 words, Guess
Page Count
636 pages
Identifiers
- Internet Archiveprobabilisticthe00devr_082
- Internet Archiveprobabilisticthe00devr_335
- Internet Archiveprobabilisticthe00devr
- ISBN-100387946187
- ISBN-139780387946184
Classifications
- DDC003/.52/015192
- LCCQ327 .D5 1996
- LCCQA273.A1-274.9
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.
Subjects
Other Editions
- A probabilistic theory of pattern recognition
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