The Leading eBooks Store Online
for Kindle Fire, Apple, Android, Nook, Kobo, PC, Mac, BlackBerry ...
Information Theoretic Learning
Renyi's Entropy and Kernel Perspectives
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.
Visual Basic in Easy Steps 2011 US$ 14.99 193 pages
Using Google Maps and Google Earth 2011 US$ 19.99 360 pages
A Practical Guide to Linux Commands, Editors, and Shell Programming, 3e 2012 US$ 39.99 1224 pages
HTML and CSS 2011 US$ 29.99 514 pages