Information Technology Reference
In-Depth Information
4. N.H. Mollah, M. Minami, S. Eguchi, Exploring latent structure of mixture ICA models by the
Minimum ß-Divergence method. Neural Comput. 18, 166-190 (2005)
5. C.T. Lin, W.C. Cheng, S.F. Liang, An on-line ICA-mixture-model-based self-constructing
fuzzy neural network. IEEE Trans. Circuits Syst. 52(1), 207-221 (2005)
6. C.A. Shah, P.K. Varshney, M.K. Arora, ICA mixture model algorithm for unsupervised
classification of remote sensing imagery. Int. J. Remote Sens. 28(8), 1711-1731 (2007)
7. A. Salazar, L. Vergara, A. Serrano, J. Igual, A general procedure for learning mixtures of
independent component analyzers. Pattern Recognit. 43(1), 69-85 (2010)
8. O. Cappe, E. Moulines, T. Ryden, Inference in Hidden Markov Models (Springer, New York,
2005)
9. A. Salazar, L. Vergara, R. Miralles, On including sequential dependence in ICA mixture
models. Signal Process. 90, 2314-2318 (2010)
10. R. Agarwal, J. Gotman, Computer-assisted sleep staging. IEEE Trans. Biomed. Eng. 12(48),
1412-1423 (2001)
11. M. Jobert, H. Shulz, P. Jähnig, C. Tismer, F. Bes, H. Escola, A computerized method for
detecting episodes of wakefulness during sleep based on the Alpha slow-wave index (ASI).
Sleep 17(1), 37-46 (1994)
12. J.F. Cardoso, A. Souloumiac, Blind beamforming for non gaussian signals. IEE Proc.-F
140(6), 362-370 (1993)
13. A.K. Jain, Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell.
22(1), 4-37 (2000)
14. J.
Srivastava,
R.
Cooley,
M.
Deshpande,
P.
Tan,
Web
usage
mining:
discovery
and
applications of usage patterns from web data. SIGKDD Explor. 2(1), 12-23 (2000)
15. J. Larsen, L.K. Hansen, A. Szymkowiak, T. Christiansen, T. Kolenda, Webmining: learning
from the World Wide Web, special issue of Computational Statistics and Data Analysis.
Comput. Stat. Data Anal. 38, 517-532 (2002)
16. S.B. Kotsiantis, C.J. Pierrakeas, P.E. Pintelas, Preventing student dropout in distance learning
using
machine
learning
techniques.
Proceedings
of
7th
International
Conference
on
Knowledge-Base Intelligent Information an Engineering Systems, pp. 267-274 (2003)
17. B. Minaei, D.A. Kashy, G. Kortemeyer, W. Punch, Predicting student performance: an
application of data mining methods with an educational web-based system. Proceedings of
33rd Frontiers in Education Conference, pp. T2A-13-T2A-18 (2003)
18. W.Zang, F. Lin, Investigation of web-based teaching and learning by boosting algorithms.
IEEE
International
Conference
on
Information
Technology:
Research
and
Education,
pp. 445-449 (2003)
19. E. Mor, J. Minguillón, E-learning personalization based on itineraries and long-term
navigational behavior. Proceedings of 30th World Web Conference, no. 2, pp. 264-265, New
York, 2004
20. M. Xenos, Prediction and assessment of student behaviour in open and distance education in
computers using Bayesian networks. Comput. Education 43, 345-359 (2004)
21. P. Garcia, A. Amandi, S. Schiaffino, M. Campo, Evaluating Bayesian Networks' Precision for
Detecting Students' Learning Styles. Comput. Education 49, 794-808 (2007)
22. R. Boscolo, H. Pan, Independent component analysis based on nonparametric density
estimation. IEEE Trans. Neural Netw. 15(1), 55-65 (2004)
23. E.G. Learned-Miller, J.W. Fisher, ICA using spacings estimates of entropy. J. Mach. Learn.
Res. 4, 1271-1295 (2003)
24. R. Felder, L. Silverman, Learning and teaching styles. J. Eng. Education 78(7), 674-681
(1988)
25. M. Khalifa, R. Lam, Web-based learning: effects on learning process and outcome. IEEE
Trans. Education 45(4), 350-356 (2002)
26. W. Hardle, L. Simar, Applied Multivariate Statistical Analysis (Springer, New York, 2006)
27. L.R.B. Elton, D.M. Laurillard, Trends in research on student learning. Stud. High. Education
4, 87-102 (1979)
Search WWH ::




Custom Search