A Probabilistic Theory of Pattern Recognition - (Stochastic Modelling and Applied Probability) by Luc Devroye & Laszlo Györfi & Gabor Lugosi
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<p/><br></br><p><b> Book Synopsis </b></p></br></br>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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