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

Principe, Jose C.

ISBN 10: 1461425859 / ISBN 13: 9781461425854

Editore: Springer, 2012

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Dati bibliografici

• Titolo: Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (Information Science and Statistics)
• Editore: Springer
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1461425859
• ISBN 13: 9781461425854
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

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.