Information theoretic learning with adaptive kernels

Signal Processing

Volumn 91, Issue 2, 2011, Pages 203-213

  Singh, Abhishek ^a   Príncipe, José C ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

Adaptive system training; Information theoretic learning; Kernel width; Kullback Leibler divergence; Minimum error entropy

Indexed keywords

ADAPTIVE SYSTEMS; COST FUNCTIONS; ENTROPY; ERRORS; THREE TERM CONTROL SYSTEMS;

ERROR ENTROPY; INFORMATION THEORETIC LEARNING; KERNEL WIDTH; KULLBACK LEIBLER DIVERGENCE; LINEAR AND NONLINEAR SYSTEMS; MINIMUM ERROR ENTROPY CRITERIONS; ON-LINE ALGORITHMS; RATE OF CONVERGENCE;

INFORMATION THEORY;

EID: 77956175555     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2010.06.023     Document Type: Article

References (19)

1. E. Walach, and B. Widrow Least mean fourth (LMF) adaptive algorithm and its family IEEE Transactions on Information Theory 30 2 1984 275 282
2. A. Barros, J. Principe, Y. Takeuchi, and N. Ohnishi Using non-linear even functions for error minimization in adaptive filters Neurocomputing 70 2006 9 13
3. A. Hyvarinen, J. Karhunen, and E. Oja Independent Component Analysis 2001 Wiley New York
4. A. Bell, and T. Sejnowski An information-maximization approach to blind source separation and blind deconvolution Neural Computation 7 1995 1129 1159
5. J. Principe, J. Fisher, D. Xu, Information theoretic learning, in: Unsupervised Adaptive Filtering, Wiley, New York, 2000, pp. 275282.
6. D. Erdogmus, and J. Principe Generalized information potential criterion for adaptive system training IEEE Transactions on Neural Networks 13 5 2002 1035 1044
7. D. Erdogmus, and J. Principe An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems IEEE Transactions on Signal Processing 50 7 2002 1780 1786
8. D. Erdogmus, and J. Principe From linear adaptive filtering to nonlinear information processing Signal Processing Magazine 23 6 2006 14 23
9. B.W. Silverman Density Estimation for Statistics and Data Analysis 1986 Chapman & Hall, CRC
10. A. Renyi Some fundamental questions of information theory Selected Papers of Alfred Renyi vol. 2 1976 Akademic Kiado Budapest
11. E. Parzen On the estimation of a probability density function and mode Annals of Mathematical Statistics 33 1962 1065 1076
12. R. Duin On the choice of smoothing parameters for Parzen estimators of probability density functions IEEE Transactions on Computers 25 7 1976 1175 1179
13. P. Hall On KullbackLeibler loss and density estimation Annals of Statistics 15 4 1987 1491 1519
14. M. Rudemo Empirical choice of histograms and kernel density estimators Scandinavian Journal of Statistics 9 9 1982 65 78
15. H. Shimazaki, and S. Shinomoto A method for selecting the bin size of a time histogram Neural Computation 19 6 2007 1503 1527
16. S. Koyama, and S. Shinomoto Histogram bin width selection for time-dependent Poisson processes Journal of Physics A: Mathematical and General 37 2004 7255 7265
17. A. Bowman An alternative method of cross-validation for the smoothing of density estimates Biometrika 71 4 1984 353 360
18. E. Lorenz Deterministic non-periodic flow Journal of the Atmospheric Sciences 20 1963 130 141
19. R. Morejon, and J. Principe Advanced search algorithms for information-theoretic learning with kernel-based estimators IEEE Transactions on Neural Networks 15 4 2004 874 884