A Probabilistic Theory of Pattern Recognition / by Luc Devroye, László Györfi, Gábor Lugosi
(Stochastic Modelling and Applied Probability ; 31)
| データ種別 | 電子ブック |
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| 出版情報 | New York, NY : Springer New York : Imprint: Springer , 1996 |
| 本文言語 | 英語 |
| 大きさ | XV, 638 p : online resource |
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| 一般注記 | 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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| 著者標目 | *Devroye, Luc author Györfi, László author Lugosi, Gábor author SpringerLink (Online service) |
| 件 名 | LCSH:Mathematics LCSH:Pattern recognition LCSH:Probabilities FREE:Mathematics FREE:Probability Theory and Stochastic Processes FREE:Pattern Recognition |
| 分 類 | DC23:519.2 |
| 巻冊次 | ISBN:9781461207115 
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| ISBN | 9781461207115 |
| URL | http://dx.doi.org/10.1007/978-1-4612-0711-5 |
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