Information Theoretic Learning Renyi's Entropy and Kernel Perspectives [Hardcover]

This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces.- Renyis 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.

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)

Jos? C. Principe is Distinguished Professor of Electrical and Biomedical Engineering, and BellSouth Professor at the University of Florida, and the Founder and Director of the Computational NeuroEngineering Laboratory. He is an IEEE and AIMBE Fellow, Past President of the International Neural Network Society, Past Editor-in-Chief of the IEEE Trans. on Biomedical Engineering and the Founder Editor-in-Chief of the IEEE Reviews on Biomedical Engineering. He has written an interactive electronilƒ"