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

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.

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“He who has felt the deepest grief is best able to experience supreme happiness. Live, then, and be happy, beloved children of my heart, and never forget, that until the day God will deign to reveal the future to man, all human wisdom is contained in these two words, ‘Wait and Hope.’" - Alexandre Dumas, The Count of Monte Cristo

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

Author: Jose C. Principe
Pages: 448
Genre(s): Mathematical statistics
Publisher: Springer
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Publication Year: 2010
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Finished? Yes, on
Signed? Yes

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