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Information Theoretic Learning

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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 8: Self-Organizing ITL Principles for Unsupervised Learning
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