A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability),New

A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability),New

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SKU: DADAX0387946187
Brand: Springer
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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 selfcontained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, VapnikChervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distributionfree 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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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

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