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Can I read A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability, 31) on EtoBox?
A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability, 31) by Luc Devroye, László Györfi, Gábor Lugosi (auth.) is a nonfiction available to read on EtoBox.
What is A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability, 31) about?
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. Erscheinungsdatum: 04.04.1996
Who reads A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability, 31)?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Luc Devroye, László Györfi, Gábor Lugosi (auth.)
- Publisher
- Springer Science & Business Media
- Published
- 1996
- Language
- EN
- ISBN
- 9781461207115
- Category
- nonfiction
- Subjects
- Mathematics, Science, Computer Science
Other editions & translations
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