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Stochastic Modelling and Applied Probability

A Probabilistic Theory of Pattern Recognition

Authors: Devroye, Luc, Györfi, László, Lugosi, Gábor

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About this Textbook

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.

Table of contents (32 chapters)

Table of contents (32 chapters)

Buy this book

eBook 67,40 €
price for Spain (gross)
  • ISBN 978-1-4612-0711-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 114,39 €
price for Spain (gross)
  • ISBN 978-0-387-94618-4
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 83,19 €
price for Spain (gross)
  • ISBN 978-1-4612-6877-2
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
A Probabilistic Theory of Pattern Recognition
Authors
Series Title
Stochastic Modelling and Applied Probability
Series Volume
31
Copyright
1996
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media, LLC, part of Springer Nature
eBook ISBN
978-1-4612-0711-5
DOI
10.1007/978-1-4612-0711-5
Hardcover ISBN
978-0-387-94618-4
Softcover ISBN
978-1-4612-6877-2
Series ISSN
0172-4568
Edition Number
1
Number of Pages
XV, 638
Topics

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