Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (Information Science and Statistics)

Principe, Jose C.

ISBN 10: 1441915699 / ISBN 13: 9781441915696

Editore: Springer, 2014

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462 pp., hardcover, new, THIS IS THE 2014 PRINTING. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Photos available upon request.

Codice articolo ZB1316476

Dati bibliografici

• Titolo: Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (Information Science and Statistics)
• Editore: Springer
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1441915699
• ISBN 13: 9781441915696
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

This bookisan outgrowthoften yearsof researchatthe Universityof Florida Computational NeuroEngineering Laboratory (CNEL) in the general area of statistical signal processing and machine learning. One of the goals of writing the book is exactly to bridge the two ?elds that share so many common problems and techniques but are not yet e?ectively collaborating. Unlikeotherbooks thatcoverthe state ofthe artinagiven?eld,this book cuts across engineering (signal processing) and statistics (machine learning) withacommontheme:learningseenfromthepointofviewofinformationt- orywithanemphasisonRenyi’sde?nitionofinformation.Thebasicapproach is to utilize the information theory descriptors of entropy and divergence as nonparametric cost functions for the design of adaptive systems in unsup- vised or supervised training modes. Hence the title: Information-Theoretic Learning (ITL). In the course of these studies, we discovered that the main idea enabling a synergistic view as well as algorithmic implementations, does not involve the conventional central moments of the data (mean and covariance). Rather, the core concept is the ?-norm of the PDF, in part- ular its expected value (? = 2), which we call the information potential. This operator and related nonparametric estimators link information theory, optimization of adaptive systems, and reproducing kernel Hilbert spaces in a simple and unconventional way.

Informazioni sull?autore

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.