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A Probabilistic Theory of Pattern Recognition. Softcover reprint of the original 1st ed. 1996.
・ISBN 978-1-4612-6877-2 paper EUR 99.99
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| 著者・編者 | Devroye, Luc / Gyoerfi, Laszlo / Lugosi, Gabor, |
|---|---|
| シリーズ | Stochastic Modelling and Applied Probability |
| 出版社 | (Springer-Verlag New York Inc., US) |
| 出版年月 | 2013 |
| ページ数 | 638 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-17143＞ |
解説
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.