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: 22.11.2013

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scihub/10.1007/978-1-4612-0711-5.pdf

zlib/Computers/Computer Science/Luc Devroye, László Györfi, Gábor Lugosi (auth.)/A Probabilistic Theory of Pattern Recognition_2296420.pdf

Devroye, Luc, Györfi, Laszlo, Lugosi, Gabor

Luc Devroye; Laszlo Györfi; Gabor Lugosi

Luc Devroye; László Györfi; Gabor Lugosi

Springer Science & Business Media

Springer London, Limited

Springer Nature (Textbooks & Major Reference Works), New York, 1996

Softcover reprint of the original 1st ed. 1996, 2013-11-22

Applications of mathematics, 31, Repr, New York, 2014

Applications of mathematics, New York, 1996

Place of publication not identified, 2014

United States, United States of America

Nov 22, 2013

sm23250022

Source title: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability (31))

Front Matter....Pages i-xv
Introduction....Pages 1-8
The Bayes Error....Pages 9-20
Inequalities and Alternate Distance Measures....Pages 21-37
Linear Discrimination....Pages 39-59
Nearest Neighbor Rules....Pages 61-90
Consistency....Pages 91-109
Slow Rates of Convergence....Pages 111-119
Error Estimation....Pages 121-132
The Regular Histogram Rule....Pages 133-145
Kernel Rules....Pages 147-167
Consistency of the k -Nearest Neighbor Rule....Pages 169-185
Vapnik-Chervonenkis Theory....Pages 187-213
Combinatorial Aspects of Vapnik-Chervonenkis Theory....Pages 215-232
Lower Bounds for Empirical Classifier Selection....Pages 233-247
The Maximum Likelihood Principle....Pages 249-262
Parametric Classification....Pages 263-278
Generalized Linear Discrimination....Pages 279-288
Complexity Regularization....Pages 289-301
Condensed and Edited Nearest Neighbor Rules....Pages 303-313
Tree Classifiers....Pages 315-362
Data-Dependent Partitioning....Pages 363-385
Splitting the Data....Pages 387-396
The Resubstitution Estimate....Pages 397-405
Deleted Estimates of the Error Probability....Pages 407-421
Automatic Kernel Rules....Pages 423-449
Automatic Nearest Neighbor Rules....Pages 451-459
Hypercubes and Discrete Spaces....Pages 461-477
Epsilon Entropy and Totally Bounded Sets....Pages 479-487
Uniform Laws of Large Numbers....Pages 489-506
Neural Networks....Pages 507-547
Other Error Estimates....Pages 549-559
Feature Extraction....Pages 561-574
Back Matter....Pages 575-638

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, tree classifiers, and neural networks

A Self-contained And Coherent Account Of Probabilistic Techniques, Covering: Distance Measures, Kernel Rules, Nearest Neighbour Rules, Vapnik-chervonenkis Theory, Parametric Classification, And Feature Extraction. Each Chapter Concludes With Problems And Exercises To Further The Readers Understanding. Both Research Workers And Graduate Students Will Benefit From This Wide-ranging And Up-to-date Account Of A Fast- Moving Field.

2014-01-18