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Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (in English)
José C. Principe
(Author)
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Springer
· Hardcover
Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (in English) - Principe, Jose C.
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Origin: Chile
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Synopsis "Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (in 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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The book is written in English.
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