A Probabilistic Theory of Pattern Recognition
(Sprache: Englisch)
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....
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Klappentext zu „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.
Inhaltsverzeichnis zu „A Probabilistic Theory of Pattern Recognition “
Preface* Introduction
* The Bayes Error
* Inequalities and alternate distance measures
* Linear discrimination
* Nearest neighbor rules
* Consistency
* Slow rates of convergence Error estimation
* The regular histogram rule
* Kernel rules Consistency of the k-nearest neighbor rule
* Vapnik-Chervonenkis theory
* Combinatorial aspects of Vapnik-Chervonenkis theory
* Lower bounds for empirical classifier selection
* The maximum likelihood principle
* Parametric classification
* Generalized linear discrimination
* Complexity regularization
* Condensed and edited nearest neighbor rules
* Tree classifiers
* Data-dependent partitioning
* Splitting the data
* The resubstitution estimate
* Deleted estimates of the error probability
* Automatic kernel rules
* Automatic nearest neighbor rules
* Hypercubes and discrete spaces
* Epsilon entropy and totally bounded sets
* Uniform laws of large numbers
* Neural networks
* Other error estimates
* Feature extraction
* Appendix
* Notation
* References
* Index
Bibliographische Angaben
- Autoren: Luc Devroye , Laszlo Györfi , Gabor Lugosi
- 1997, 1st ed. 1996. Corr. 2nd printing 1997, 638 Seiten, Masse: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, New York
- ISBN-10: 0387946187
- ISBN-13: 9780387946184
- Erscheinungsdatum: 20.02.1997
Sprache:
Englisch
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