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A Probabilistic Theory of Pattern Recognition

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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 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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781461268772
  • Indbinding:
  • Paperback
  • Sideantal:
  • 660
  • Udgivet:
  • 22. november 2013
  • Størrelse:
  • 155x36x235 mm.
  • Vægt:
  • 984 g.
  • 8-11 hverdage.
  • 28. november 2024
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Beskrivelse af A Probabilistic Theory of Pattern Recognition

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.

Brugerbedømmelser af A Probabilistic Theory of Pattern Recognition