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140122 ||| eng |
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|a 9781461207115
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| 100 |
1 |
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|a Devroye, Luc
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| 245 |
0 |
0 |
|a A Probabilistic Theory of Pattern Recognition
|h Elektronische Ressource
|c by Luc Devroye, Laszlo Györfi, Gabor Lugosi
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| 250 |
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|a 1st ed. 1996
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| 260 |
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|a New York, NY
|b Springer New York
|c 1996, 1996
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| 300 |
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|a XV, 638 p
|b online resource
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| 653 |
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|a Probability Theory
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Probabilities
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| 653 |
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|a Pattern recognition systems
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| 700 |
1 |
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|a Györfi, Laszlo
|e [author]
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| 700 |
1 |
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|a Lugosi, Gabor
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
0 |
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|a Stochastic Modelling and Applied Probability
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| 028 |
5 |
0 |
|a 10.1007/978-1-4612-0711-5
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4612-0711-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.2
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| 520 |
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|a 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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