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.

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Bibliographic Details

Title: A Probabilistic Theory of Pattern Recognition
Publisher: Springer New York, Springer New York
Publication Date: 2013
Language: English
ISBN 10: 146126877X
ISBN 13: 9781461268772
Binding: Taschenbuch
Condition: Neu
Dimensions: 235 millimeters width by 155 millimeters height by 36 millimeters depth
Weight: 984 grams

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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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