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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

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    Book Overview
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    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
Overall attention for this book and its chapters
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624 Mendeley
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Title
A Probabilistic Theory of Pattern Recognition
Published by
Springer New York, November 2013
DOI 10.1007/978-1-4612-0711-5
ISBNs
978-1-4612-0711-5, 978-1-4612-6877-2
Authors

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

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 624 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 22 4%
United Kingdom 5 <1%
Brazil 4 <1%
France 4 <1%
Germany 4 <1%
Canada 4 <1%
India 3 <1%
Poland 3 <1%
China 3 <1%
Other 24 4%
Unknown 548 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 200 32%
Researcher 106 17%
Student > Master 87 14%
Student > Bachelor 42 7%
Professor > Associate Professor 40 6%
Other 149 24%
Readers by discipline Count As %
Computer Science 277 44%
Engineering 93 15%
Mathematics 85 14%
Unspecified 43 7%
Agricultural and Biological Sciences 25 4%
Other 101 16%