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A Probabilistic Theory of Pattern Recognition
other
Author(s):
Luc Devroye
,
László Györfi
,
Gábor Lugosi
Publication date
(Print):
1996
Publisher:
Springer New York
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Related collections
Numerical Algebra, Matrix Theory, Differential-Algebraic Equations, and Control Theory
Author and book information
Book
ISBN (Print):
978-1-4612-6877-2
ISBN (Electronic):
978-1-4612-0711-5
Publication date (Print):
1996
DOI:
10.1007/978-1-4612-0711-5
SO-VID:
f001c53d-0400-4ccd-b6eb-09a5345f9922
License:
http://www.springer.com/tdm
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Book chapters
pp. 1
Introduction
pp. 9
The Bayes Error
pp. 21
Inequalities and Alternate Distance Measures
pp. 39
Linear Discrimination
pp. 61
Nearest Neighbor Rules
pp. 91
Consistency
pp. 111
Slow Rates of Convergence
pp. 121
Error Estimation
pp. 133
The Regular Histogram Rule
pp. 147
Kernel Rules
pp. 169
Consistency of the k-Nearest Neighbor Rule
pp. 187
Vapnik-Chervonenkis Theory
pp. 215
Combinatorial Aspects of Vapnik-Chervonenkis Theory
pp. 233
Lower Bounds for Empirical Classifier Selection
pp. 249
The Maximum Likelihood Principle
pp. 263
Parametric Classification
pp. 279
Generalized Linear Discrimination
pp. 289
Complexity Regularization
pp. 303
Condensed and Edited Nearest Neighbor Rules
pp. 315
Tree Classifiers
pp. 363
Data-Dependent Partitioning
pp. 387
Splitting the Data
pp. 397
The Resubstitution Estimate
pp. 407
Deleted Estimates of the Error Probability
pp. 423
Automatic Kernel Rules
pp. 451
Automatic Nearest Neighbor Rules
pp. 461
Hypercubes and Discrete Spaces
pp. 479
Epsilon Entropy and Totally Bounded Sets
pp. 489
Uniform Laws of Large Numbers
pp. 507
Neural Networks
pp. 549
Other Error Estimates
pp. 561
Feature Extraction
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