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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 7: Clustering with ITL Principles
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Citations

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Chapter title
Clustering with ITL Principles
Chapter number 7
Book title
Information Theoretic Learning
Published by
Springer, New York, NY, January 2010
DOI 10.1007/978-1-4419-1570-2_7
Book ISBNs
978-1-4419-1569-6, 978-1-4419-1570-2
Authors

Robert Jenssen, Sudhir Rao, Jenssen, Robert, Rao, Sudhir

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 33%
Student > Ph. D. Student 1 33%
Student > Master 1 33%
Readers by discipline Count As %
Engineering 2 67%
Unknown 1 33%

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