INFORMATION THEORETIC LEARNING: RENYI'S ENTROPY AND ITS APPLICATIONS TO ADAPTIVE SYSTEM TRAINING
作者： Deniz Erdogmus
DOI:
关键词: Adaptive system 、 Mathematics 、 Entropy (information theory) 、 Theoretical computer science
摘要:
neu.edu 参考文章(125)
Deniz Erdogmus, Jose C. Principe, COMPARISON OF ENTROPY AND MEAN SQUARE ERROR CRITERIA IN ADAPTIVE SYSTEM TRAINING USING HIGHER ORDER STATISTICS ,(2004)
Jose C. Principe, Dongxin Xu, Energy, entropy and information potential for neural computation University of Florida. ,(1998)
Jose C. Principe, John W. Fisher, Nonlinear extensions to the minimum average correlation energy filter University of Florida. ,(1997)
Jose C. Principe, Hsiao-Chun Wu, Blind source separation using information measures in the time and frequency domains University of Florida. ,(1999)
J. C. Príncipe, W. Curt Lefebvre, Neil R. Euliano, Neural and adaptive systems : fundamentals through simulations Wiley. ,(2000)
Deniz Erdogmus, José Carlos Príncipe, Insights on the relationship between probability of misclassification and information transfer through classifiers. The International Journal of Computers, Systems and Signal. ,vol. 3, pp. 40- 54 ,(2002)
S Haykin, Adaptive Filter Theory ,(1986)
David Donoho, ON MINIMUM ENTROPY DECONVOLUTION Applied Time Series Analysis II#R##N#Proceedings of the Second Applied Time Series Symposium Held in Tulsa, Oklahoma, March 3–5, 1980. pp. 565- 608 ,(1981) , 10.1016/B978-0-12-256420-8.50024-1
Seungjin Choi, Andrzej Cichocki, Shun-Ichi Amari, Flexible Independent Component Analysis signal processing systems. ,vol. 26, pp. 25- 38 ,(2000) , 10.1023/A:1008135131269
Shun-ichi Amari, Differential-geometrical methods in statistics ,(1985)