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A Probabilistic Theory of Pattern Recognition

  • Textbook
  • © 1996

Overview

Authors:
  1. Luc Devroye

    1. School of Computer Science, McGill University, Montreal, Canada

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  2. László Györfi

    1. Department of Mathematics and Computer Science, Technical University of Budapest, Budapest, Hungary

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  3. Gábor Lugosi

    1. Department of Mathematics and Computer Science, Technical University of Budapest, Budapest, Hungary

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Part of the book series: Stochastic Modelling and Applied Probability (SMAP, volume 31)

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About this book

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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Table of contents (32 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Introduction

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 1-8

  3. The Bayes Error

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 9-20

  4. Inequalities and Alternate Distance Measures

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 21-37

  5. Linear Discrimination

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 39-59

  6. Nearest Neighbor Rules

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 61-90

  7. Consistency

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 91-109

  8. Slow Rates of Convergence

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 111-119

  9. Error Estimation

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 121-132

  10. The Regular Histogram Rule

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 133-145

  11. Kernel Rules

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 147-167

  12. Consistency of the k-Nearest Neighbor Rule

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 169-185

  13. Vapnik-Chervonenkis Theory

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 187-213

  14. Combinatorial Aspects of Vapnik-Chervonenkis Theory

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 215-232

  15. Lower Bounds for Empirical Classifier Selection

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 233-247

  16. The Maximum Likelihood Principle

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 249-262

  17. Parametric Classification

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 263-278

  18. Generalized Linear Discrimination

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 279-288

  19. Complexity Regularization

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 289-301

  20. Condensed and Edited Nearest Neighbor Rules

    • Luc Devroye, László Györfi, Gábor Lugosi
    Pages 303-313
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Authors and Affiliations

  • School of Computer Science, McGill University, Montreal, Canada

    Luc Devroye

  • Department of Mathematics and Computer Science, Technical University of Budapest, Budapest, Hungary

    László Györfi, Gábor Lugosi

Bibliographic Information

  • Book Title: A Probabilistic Theory of Pattern Recognition

  • Authors: Luc Devroye, László Györfi, Gábor Lugosi

  • Series Title: Stochastic Modelling and Applied Probability

  • DOI: https://doi.org/10.1007/978-1-4612-0711-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 1996

  • Hardcover ISBN: 978-0-387-94618-4Published: 04 April 1996

  • Softcover ISBN: 978-1-4612-6877-2Published: 22 November 2013

  • eBook ISBN: 978-1-4612-0711-5Published: 27 November 2013

  • Series ISSN: 0172-4568

  • Series E-ISSN: 2197-439X

  • Edition Number: 1

  • Number of Pages: XV, 638

  • Topics: Probability Theory and Stochastic Processes, Pattern Recognition

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