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A Probabilistic Theory of Pattern Recognition

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Cover of 'A Probabilistic Theory of Pattern Recognition'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction
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    Chapter 2 The Bayes Error
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    Chapter 3 Inequalities and Alternate Distance Measures
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    Chapter 4 Linear Discrimination
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    Chapter 5 Nearest Neighbor Rules
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    Chapter 6 Consistency
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    Chapter 7 Slow Rates of Convergence
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    Chapter 8 Error Estimation
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    Chapter 9 The Regular Histogram Rule
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    Chapter 10 Kernel Rules
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    Chapter 11 Consistency of the k -Nearest Neighbor Rule
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    Chapter 12 Vapnik-Chervonenkis Theory
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    Chapter 13 Combinatorial Aspects of Vapnik-Chervonenkis Theory
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    Chapter 14 Lower Bounds for Empirical Classifier Selection
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    Chapter 15 The Maximum Likelihood Principle
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    Chapter 16 Parametric Classification
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    Chapter 17 Generalized Linear Discrimination
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    Chapter 18 Complexity Regularization
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    Chapter 19 Condensed and Edited Nearest Neighbor Rules
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    Chapter 20 Tree Classifiers
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    Chapter 21 Data-Dependent Partitioning
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    Chapter 22 Splitting the Data
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    Chapter 23 The Resubstitution Estimate
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    Chapter 24 Deleted Estimates of the Error Probability
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    Chapter 25 Automatic Kernel Rules
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    Chapter 26 Automatic Nearest Neighbor Rules
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    Chapter 27 Hypercubes and Discrete Spaces
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    Chapter 28 Epsilon Entropy and Totally Bounded Sets
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    Chapter 29 Uniform Laws of Large Numbers
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    Chapter 30 Neural Networks
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    Chapter 31 Other Error Estimates
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    Chapter 32 Feature Extraction
Attention for Chapter 30: Neural Networks
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Chapter title
Neural Networks
Chapter number 30
Book title
A Probabilistic Theory of Pattern Recognition
Published by
Springer, New York, NY, January 1996
DOI 10.1007/978-1-4612-0711-5_30
Book ISBNs
978-1-4612-6877-2, 978-1-4612-0711-5
Authors

Luc Devroye, László Györfi, Gábor Lugosi, Devroye, Luc, Györfi, László, Lugosi, Gábor

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