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A Probabilistic Theory of Pattern Recognition: 31 (Stochastic Modelling and Applied Probability, 31) - Hardcover
Synopsis
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
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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.
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- PublisherSpringer
- Publication date1996
- ISBN 10 0387946187
- ISBN 13 9780387946184
- BindingHardcover
- LanguageEnglish
- Number of pages653
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APPLICATIONS OF MATHEMATICS- A PROBABILISTIC THEORY OF PATTERN RECOGNITION, SIE
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Applications Of Mathematics- A Probabilistic Theory Of Pattern Recognition, Sie
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A probabilistic theory of pattern recognition Luc Devroye, László Györfi, Gábor Lugosi. Applications of mathematics 31; Applications of Mathematics 31.
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A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)
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