Information Technology Reference
In-Depth Information
one included in the second one, the second one included in the third one and
so on). Once the histogram data have been represented by interval data, our
proposed approaches can naturally deal with histogram data.
A forthcoming improvement will be to extend our approaches to taxonom-
ical or mixed data types. Another one will be to use high-level representative
data for tuning the SVM parameters. This approach drastically reduces the cost
compared with the research in initial large datasets.
References
1. Fayyad, U., Piatetsky-Shapiro, G., Uthurusamy, R.: Summary from the kdd-03
panel - data mining: The next 10 years. SIGKDD Explorations 5(2), 191-196 (2004)
2. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge
discovery in databases. AI Magazine 17(3), 37-54 (1996)
3. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
4. Guyon, I.: Svm application list (1999),
http://www.clopinet.com/isabelle/Projects/SVM/applist.html
5. Bock, H., Diday, E.: Analysis of Symbolic Data. Springer, Heidelberg (2000)
6. Do, T., Poulet, F.: Enhancing svm with visualization. In: Int. Conf. on Discovery
Science, pp. 183-194 (2004)
7. Do, T., Poulet, F.: Very large datasets with svm and visualization. In: Int. Conf.
on Entreprise Information Systems, pp. 127-134 (2005)
8. Poulet, F.: Svm and graphical algorithms: a cooperative approach. In: Proceedings
of IEEE International Conference on Data Mining, pp. 499-502 (2004)
9. MacQueen, J.: Some methods for classification and analysis of multivariate ob-
servations. In: Berkeley Symposium on Mathematical Statistics and Probability,
vol. (1), pp. 281-297. University of California Press (1967)
10. Lin, C.: A practical guide to support vector classification (2003)
11. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Muller, K.R.: Fisher discriminant
analysis with kernels. Neural Networks for Signal Processing IX, 41-48 (1999)
12. Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel
eigenvalue problem. Neural Computation 10, 1299-1319 (1998)
13. Rosipal, R., Trejo, L.J.: Kernel partial least squares regression in reproducing ker-
nel hilbert space. Journal of Machine Learning Research 2, 97-123 (2001)
14. Bennett, K., Campbell, C.: Support vector machines: Hype or hallelujah? SIGKDD
Explorations 2(2), 1-13 (2000)
15. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and
Other Kernel-based Learning Methods. Cambridge University Press, Cambridge
(2000)
16. Chang, C., Lin, C.: Libsvm - a library for support vector machines (2001),
http://www.csie.ntu.edu.tw/ cjlin/libsvm/
17. Michie, D., Spiegelhalter, D.J., Taylor, C.: Machine Learning, Neural and Statis-
tical Classification. Ellis Horwood (1994)
18. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998),
http://www.ics.uci.edu/ mlearn/MLRepository.html
19. Torgo, L.: Regression data sets (2003),
http://www.liacc.up.pt/ ltorgo/Regression/DataSets.html
20. Delve: Data for evaluating learning in valid experiments (1996),
http://www.cs.toronto.edu/ delve
Search WWH ::




Custom Search