The Leading eBooks Store Online

for Kindle Fire, Apple, Android, Nook, Kobo, PC, Mac, BlackBerry ...

New to eBooks.com?

Learn more

Information Theoretic Learning

Renyi's Entropy and Kernel Perspectives

Information Theoretic Learning by Jose C. Principe
Not available
This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by scalars and functions with information theoretic underpinnings, respectively entropy, mutual information and correntropy. ITL quantifies the stochastic structure of the data beyond second order statistics for improved performance without using full-blown Bayesian approaches that require a much larger computational cost. This is possible because of a non-parametric estimator of Renyi's quadratic entropy that is only a function of pairwise differences between samples. The book compares the performance of ITL algorithms with the second order counterparts in many engineering and machine learning applications.
Springer; Read online
Title: Information Theoretic Learning
Author: Jose C. Principe
 
Buy, download and read Information Theoretic Learning (eBook) by Jose C. Principe today!