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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语言 英语 ● 格式 PDF ● ISBN 9781461207115 ● 出版者 Springer New York ● 发布时间 2013 ● 下载 3 时 ● 货币 EUR ● ID 4720836 ● 复制保护 Adobe DRM
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