Information Theoretic Learning: Reny's Entropy and Kernel Perspectives (Principe, J.; 2010) [Book Review

Abstract

This book, derived from Jose Principe and his group's 10 years' research in information theory and statistical learning, gives a comprehensive introduction, analysis and demonstration of almost all the major components required for understanding and developing the new theme of information-theoretical learning. The basic strategy utilized by the author is to apply information theory descriptors (namely entropy and divergence, in contrast to the statistical measures of mean and covariance)as nonparametric cost functions for the design of adaptive systems, thus creating a new paradigm of information theoretic learning. And like in statistical learning, unsupervised or supervised training modes are also fully explored.