A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability, Band 31) - Softcover
Buch 1 von 30: Stochastic Modelling and Applied Probability
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Pattern recognition presents a significant challege for scientists and engineers, and many different approaches have been proposed. This book provides a self-contained account of probabilistic techniques that have been applied to the subject. Researchers and graduate students will benefit from this wide-ranging account of the field.
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A Probabilistic Theory of Pattern Recognition
Verlag:
Springer New York, Springer New York Nov 2013, 2013
ISBN 10: 146126877X
ISBN 13: 9781461268772
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Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 660 pp. Englisch. Artikel-Nr. 9781461268772
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A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)
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Paperback. Zustand: Brand New. reprint edition. 653 pages. 9.25x6.10x1.42 inches. In Stock. Artikel-Nr. x-146126877X
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A Probabilistic Theory of Pattern Recognition
Verlag:
Springer New York, Springer New York, 2013
ISBN 10: 146126877X
ISBN 13: 9781461268772
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9781461268772
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