Jose C. Principe 
Information Theoretic Learning [PDF ebook] 
Renyi’s Entropy and Kernel Perspectives

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Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces.- Renyi’s Entropy, Divergence and Their Nonparametric Estimators.- Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria.- Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems.- Nonlinear Adaptive Filtering with MEE, MCC, and Applications.- Classification with EEC, Divergence Measures, and Error Bounds.- Clustering with ITL Principles.- Self-Organizing ITL Principles for Unsupervised Learning.- A Reproducing Kernel Hilbert Space Framework for ITL.- Correntropy for Random Variables: Properties and Applications in Statistical Inference.- Correntropy for Random Processes: Properties and Applications in Signal Processing.

Mengenai Pengarang

José C. Principe is Distinguished Professor of Electrical and Biomedical Engineering, and Bell South Professor at the University of Florida, and the Founder and Director of the Computational Neuro Engineering 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.

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Bahasa Inggeris ● Format PDF ● Halaman-halaman 448 ● ISBN 9781441915702 ● Saiz fail 10.8 MB ● Penerbit Springer New York ● Bandar raya NY ● Negara US ● Diterbitkan 2010 ● Muat turun 24 bulan ● Mata wang EUR ● ID 2150006 ● Salin perlindungan Social DRM

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