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A Probabilistic Theory of Pattern Recognition / by Luc Devroye, Laszlo Györfi, Gabor Lugosi
(Stochastic Modelling and Applied Probability. ISSN:2197439X ; 31)
Edition | 1st ed. 1996. |
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Publisher | (New York, NY : Springer New York : Imprint: Springer) |
Year | 1996 |
Language | English |
Size | XV, 638 p : online resource |
Authors | *Devroye, Luc author Györfi, Laszlo author Lugosi, Gabor author SpringerLink (Online service) |
Subjects | LCSH:Probabilities LCSH:Pattern recognition systems FREE:Probability Theory FREE:Automated Pattern Recognition |
Notes | 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 HTTP:URL=https://doi.org/10.1007/978-1-4612-0711-5 |
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E-Book | Location | Media type | Volume | Call No. | Status | Reserve | Comments | ISBN | Printed | Restriction | Designated Book | Barcode No. |
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E-Book | オンライン | 電子ブック |
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Springer eBooks | 9781461207115 |
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電子リソース |
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EB00227989 |
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Material Type | E-Book |
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Classification | LCC:QA273.A1-274.9 DC23:519.2 |
ID | 4000105071 |
ISBN | 9781461207115 |
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