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Information Theoretic Learning

Overview of attention for book
Cover of 'Information Theoretic Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces
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    Chapter 2 Renyi’s Entropy, Divergence and Their Nonparametric Estimators
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    Chapter 3 Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria
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    Chapter 4 Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems
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    Chapter 5 Nonlinear Adaptive Filtering with MEE, MCC, and Applications
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    Chapter 6 Classification with EEC, Divergence Measures, and Error Bounds
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    Chapter 7 Clustering with ITL Principles
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    Chapter 8 Self-Organizing ITL Principles for Unsupervised Learning
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    Chapter 9 A Reproducing Kernel Hilbert Space Framework for ITL
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    Chapter 10 Correntropy for Random Variables: Properties and Applications in Statistical Inference
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    Chapter 11 Correntropy for Random Processes: Properties and Applications in Signal Processing
Attention for Chapter 6: Classification with EEC, Divergence Measures, and Error Bounds
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Citations

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Chapter title
Classification with EEC, Divergence Measures, and Error Bounds
Chapter number 6
Book title
Information Theoretic Learning
Published by
Springer, New York, NY, January 2010
DOI 10.1007/978-1-4419-1570-2_6
Book ISBNs
978-1-4419-1569-6, 978-1-4419-1570-2
Authors

Deniz Erdogmus, Dongxin Xu, Kenneth Hild, Erdogmus, Deniz, Xu, Dongxin, Hild, Kenneth

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 50%
Student > Ph. D. Student 1 25%
Professor 1 25%
Readers by discipline Count As %
Engineering 3 75%
Unknown 1 25%

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