Information Technology Reference
In-Depth Information
12. W. Liu, D.P. Mandic, A. Cichocki, Blind source extraction based on a linear predictor. IET
Signal Process. 1(1), 29-34 (2007)
13. S.I. Amari, T.P. Chen, A. Cichocki, Nonholonomic orthogonal learning algorithms for blind
source separation. Neural Comput. 12, 1463-1484 (2000)
14. T.W. Lee, M. Girolami, T.J. Sejnowski, Independent component analysis using an extended
InfoMax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Comput.
11(2), 417-441 (1999)
15. D. de Ridder, J. Kittler, R.P.W. Duin, Probabilistic PCA and ICA subspace mixture models
for image segmentation, in Proceedings of the British Machine Vision Conference, Bristol
(2000), pp. 112-121
16. A. Hyvärinen, E. Oja, A fast fixed-point algorithm for independent component analysis.
Neural Comput. 9(7), 1483-1492 (1998)
17. J.F. Cardoso, A. Souloumiac, Blind beamforming for non gaussian signals. IEE Proc. F
140(6), 362-370 (1993)
18. A. Ziehe, K.R. Müller, TDSEP—an efficient algorithm for blind separation using time
structure, in Proceedings of the 8th International Conference on Artificial Neural Networks,
ICANN'98, Perspectives in Neural Computing (1998), pp. 675-680
19. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and
blind deconvolution. Neural Comput. 7, 1129-1159 (1995)
20. F.R. Bach, M.I. Jordan, Kernel independent component analysis. J. Mach. Learn. Res. 3,
1-48 (2002)
21. R. Boscolo, H. Pan, Independent component analysis based on nonparametric density
estimation. IEEE Trans. Neural Netw. 15(1), 55-65 (2004)
22. E.G. Learned-Miller, J.W. Fisher, ICA using spacings estimates of entropy. J. Mach. Learn.
Res. 4, 1271-1295 (2003)
23. S.
Haykin,
Neural
Networks—A
comprehensive
Foundation,
2nd
edn.
(Prentice-Hall,
Englewood Cliffs, 1998)
24. A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis (Wiley, New York,
2001)
25. H. Lappalainen, A. Honkela, Bayesian nonlinear independent component analysis by multi-
layer perceptrons, in Advances in Independent Component Analysis, ed. by M. Girolami
(Springer, Berlin, 2000), pp. 93-121
26. C.M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford,
2004)
27. B. Shahshahani, D. Landgrebe, Effect of unlabelled samples in reducing the small sample
size problem and mitigating the Hughes phenomenon. IEEE Trans. Geosci. Remote Sens.
32(5), 1087-1095 (1994)
28. S. Baluja, Probabilistic modelling for face orientation discrimination: learning from labelled
and unlabelled data, in Proceedings of the Neural Information and Processing Systems
(NIPS) (1998), pp. 854-860
29. T. Mitchell, The role of unlabelled data in supervised learning, in Proceedings of the Sixth
Int'l Colloquium Cognitive Science (1999)
30. K. Nigam, A.K. McCallum, S. Thrun, T. Mitchell, Text classification from labelled and
unlabelled documents using EM. Mach. Learn. 39, 103-134 (2000)
31. I. Cohen, F.G. Cozman, N. Sebe, M.C. Cirelo, T.S. Huang, Semisupervised learning of
classifiers: theory, algorithms, and their application to human-computer interaction. IEEE
Trans. Pattern Anal. Mach. Learn. 26(12), 1553-1567 (2004)
32. V. Barnet, T. Lewis, Outliers in statistical data (Wiley, New York, 1994)
33. J.C. Bezdek, S.K. Pal, Fuzzy models for pattern recognition: methods that search for
structures in data (IEEE Press, New York, 1992)
34. R.S. Raghavan, A method for estimating parameters of K-distributed clutter. IEEE Trans.
Aerosp. Electron. Syst. 27(2), 268-275 (1991)
35. D.J. Mackay, Information theory, inference and learning algorithms (Cambridge University
Press, Cambridge, 2004)
Search WWH ::




Custom Search