Jose C. Principe

Information Theoretic Learning (eBook, PDF)

Renyi's Entropy and Kernel Perspectives

• Format: PDF

Produktdetails

• Verlag: Springer New York
• Erscheinungstermin: 6. April 2010
• Englisch
• ISBN-13: 9781441915702
• Artikelnr.: 37347427

Autorenporträt

Inhaltsangabe

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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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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.- Appendix A: PDF estimation methods and experimental evaluation of ITL descriptors.

Rezensionen

From the book reviews:

"The book is remarkable in various ways in the information it presents on the concept and use of entropy functions and their applications in signal processing and solution of statistical problems such as M-estimation, classification, and clustering. Students of engineering and statistics will greatly benefit by reading it." (C. R. Rao, Technometrics, Vol. 55 (1), February, 2013)