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Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
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Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives

by Jose C Principe
No customer reviews yet ISBN 9781441915696
Published
Apr 2010
Language
English
$228.98
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About this book

Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces.- Renyi's Entropy, Divergence and Their Nonparametric Estimators.- Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria.- Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems.- Nonlinear Adaptive Filtering with MEE, MCC, and Applications.- Classification with EEC, Divergence Measures, and Error Bounds.- Clustering with ITL Principles.- Self-Organizing ITL Principles for Unsupervised Learning.- A Reproducing Kernel Hilbert Space Framework for ITL.- Correntropy for Random Variables: Properties and Applications in Statistical Inference.- Correntropy for Random Processes: Properties and Applications in Signal Processing.

Specifications

PublishedApr 15, 2010
ISBN-101441915699
ISBN-139781441915696
LanguageEnglish

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