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A Probabilistic Theory of Pattern Recognition: 31 (Stochastic Modelling and Applied Probability) - Hardcover
Inhaltsangabe
Book by Devroye Luc Gyrfi Laszlo Lugosi Gabor
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Reseña del editor
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.
Contraportada
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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- VerlagSpringer
- Erscheinungsdatum1997
- ISBN 10 0387946187
- ISBN 13 9780387946184
- EinbandTapa dura
- SpracheEnglisch
- Anzahl der Seiten660
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A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)
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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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