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Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives

Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives

Paperback

Series: Information Science and Statistics

Technology & EngineeringGeneral Computers

ISBN10: 1461425859
ISBN13: 9781461425854
Publisher: Springer
Published: May 27 2012
Pages: 448
Weight: 1.68
Height: 1.12 Width: 6.14 Depth: 9.21
Language: English
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.

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