A Probabilistic Theory of Pattern Recognition

Authors:

ISBN: 978-1-4612-6877-2 (Print) 978-1-4612-0711-5 (Online)

Table of contents (32 chapters)

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  1. Front Matter

    Pages i-xv

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    Chapter

    Pages 1-8

    Introduction

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    Chapter

    Pages 9-20

    The Bayes Error

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    Chapter

    Pages 21-37

    Inequalities and Alternate Distance Measures

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    Chapter

    Pages 39-59

    Linear Discrimination

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    Chapter

    Pages 61-90

    Nearest Neighbor Rules

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    Chapter

    Pages 91-109

    Consistency

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    Chapter

    Pages 111-119

    Slow Rates of Convergence

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    Chapter

    Pages 121-132

    Error Estimation

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    Chapter

    Pages 133-145

    The Regular Histogram Rule

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    Chapter

    Pages 147-167

    Kernel Rules

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    Chapter

    Pages 169-185

    Consistency of the k-Nearest Neighbor Rule

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    Chapter

    Pages 187-213

    Vapnik-Chervonenkis Theory

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    Chapter

    Pages 215-232

    Combinatorial Aspects of Vapnik-Chervonenkis Theory

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    Chapter

    Pages 233-247

    Lower Bounds for Empirical Classifier Selection

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    Chapter

    Pages 249-262

    The Maximum Likelihood Principle

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    Chapter

    Pages 263-278

    Parametric Classification

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    Chapter

    Pages 279-288

    Generalized Linear Discrimination

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    Chapter

    Pages 289-301

    Complexity Regularization

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    Chapter

    Pages 303-313

    Condensed and Edited Nearest Neighbor Rules

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    Chapter

    Pages 315-362

    Tree Classifiers

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