A probabilistic theory of pattern recognition
by Luc Devroye
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.
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Previews available in: English
| Edition | Availability |
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1
A Probabilistic Theory of Pattern Recognition
Nov 22, 2013, Springer
paperback
146126877X 9781461268772
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2
A Probabilistic Theory of Pattern Recognition
Dec 02, 2013, Springer
paperback
1461207126 9781461207122
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3
Probabilistic Theory of Pattern Recognition
2013, Springer London, Limited
in English
1461207118 9781461207115
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Includes bibliographical references (p. [593]-618) and indexes.
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