Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Information Science and Statistics (ISS)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
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.
Students, practitioners and researchers interested in statistical signal processing, computational intelligence, and machine learning will find in this book the theory to understand the basics, the algorithms to implement applications, and exciting but still unexplored leads that will provide fertile ground for future research.
José 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 electronic book on Neural Networks, a book on Brain Machine Interface Engineering and more recently a book on Kernel Adaptive Filtering, and was awarded the 2011 IEEE NeuralNetwork Pioneer Award.
Similar content being viewed by others
Keywords
Table of contents (11 chapters)
Reviews
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)
Authors and Affiliations
About the author
José 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 electronic book on Neural Networks, a book on Brain Machine Interface Engineering and more recently a book on Kernel Adaptive Filtering, and was awarded the 2011 IEEE Neural Network Pioneer Award.
Bibliographic Information
Book Title: Information Theoretic Learning
Book Subtitle: Renyi's Entropy and Kernel Perspectives
Authors: Jose C. Principe
Series Title: Information Science and Statistics
DOI: https://doi.org/10.1007/978-1-4419-1570-2
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag New York 2010
Hardcover ISBN: 978-1-4419-1569-6Published: 15 April 2010
Softcover ISBN: 978-1-4614-2585-4Published: 27 May 2012
eBook ISBN: 978-1-4419-1570-2Published: 06 April 2010
Series ISSN: 1613-9011
Series E-ISSN: 2197-4128
Edition Number: 1
Number of Pages: XIV, 448
Topics: Theory of Computation, Artificial Intelligence, Signal, Image and Speech Processing, Computational Intelligence, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Remote Sensing/Photogrammetry