A probabilistic theory of pattern recognition /
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 me...
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
New York :
Springer,
[1996]
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| Series: | Applications of mathematics ;
31. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Physical Description: | 1 online resource (xv, 636 pages) : illustrations. |
| Bibliography: | Includes bibliographical references (pages [593]-618) and indexes. |
| ISBN: | 9781461207115 (electronic bk.) 1461207118 (electronic bk.) |