A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)
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Book Description
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.
Book Info
Provides a self-contained account of probabilistic analysis of pattern recognition. Text 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, & neural networks. DLC: Pattern perception.
A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability),Luc Devroye,Laszlo Györfi,Gabor Lugosi,Springer,0387946187,Mathematics,Pattern perception,Probabilities,Probability & Statistics - General,Science/Mathematics,Applied mathematics,Mathematics / Statistics,Pattern recognition,Probability & statistics
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