A Probabilistic Theory of Pattern Recognition: 31 (Stochastic Modelling and Applied Probability) - Hardcover
Buch 1 von 30: Stochastic Modelling and Applied Probability
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A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
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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, tree 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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Applications Of Mathematics- A Probabilistic Theory Of Pattern Recognition, Sie
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A Probabilistic Theory of Pattern Recognition
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A Probabilistic Theory of Pattern Recognition
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Zustand: Sehr gut. 636 Seiten Zustand: Einband etwas berieben, Name auf fliegendem Vorsatzblatt // Mit 99 Abbildungen. Englische Ausgabe /// Versand gratis Innerhalb Deutschlands - Portofrei in Deutschland- ab 20 Euro mit Post ID - Gratisversand deutschlandweit innerhalb Deutschlands gratis Versand -Versandkostenfrei innerhalb Deutschlands /// Sprache: Englisch Gewicht in Gramm: 1030 24,5 x 16,0 cm, Hardcover/Pappeinband. Artikel-Nr. 354304
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A Probabilistic Theory of Pattern Recognition
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Springer New York, Springer New York Apr 1996, 1996
ISBN 10: 0387946187
ISBN 13: 9780387946184
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Buch. 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. 9780387946184
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A Probabilistic Theory of Pattern Recognition
Verlag:
Springer New York, Springer New York, 1996
ISBN 10: 0387946187
ISBN 13: 9780387946184
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Buch. 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. 9780387946184
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