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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.
| Autorius: | Luc Devroye, Gabor Lugosi, Laszlo Györfi, |
| Serija: | Stochastic Modelling and Applied Probability |
| Leidėjas: | Springer New York |
| Išleidimo metai: | 1996 |
| Knygos puslapių skaičius: | 660 |
| ISBN-10: | 0387946187 |
| ISBN-13: | 9780387946184 |
| Formatas: | 241 x 160 x 40 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „A Probabilistic Theory of Pattern Recognition“
