Classifying very large data sets with minimum enclosing ball based support vector machine

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2006, Pages 364-368

  Jin, Bo ^a,b   Zhang, Yan Qing ^a,b  

^a IEEE   (United States)

^b GEORGIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; MINIMUM ENCLOSING BALL (MEB); RINGNORM ARTIFICIAL DATA SET;

BOUNDARY CONDITIONS; CLASSIFICATION (OF INFORMATION); INTRUSION DETECTION; SUPPORT VECTOR MACHINES;

DATA STRUCTURES;

EID: 34250705697     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2006.1681738     Document Type: Conference Paper

References (16)

1. B. Boser, I. Guyon, and V.N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. Fifth Annual Workshop on Computational Learning Theory, 1992, pp. 144-152.
2. C. Cortes and V.N. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
3. V.N. Vapnik, Statistical Learning Theory. New York:John Wiley and Sons, 1998.
4. T. Joachims, "Making large-scale support vector machine learning practical," in Advances in Kernel Methods: Support Vector Machin. A. S.B. Scholkopf and C. Burges, Ed. Cambridge, MA: MIT Press, 1998.
5. J. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel Methods: Support Vector Machin. A.S.B. Scholkopf and C. Burges, Ed. Cambridge, MA: MIT Press, 1998.
6. H. Yu, J. Yang, and J. Han, "Classifying large data sets using SVMs with hierarchical clusters," in Proc. ACM Int'l Conf. Knowledge Disc. Data Mining (KDD), 2003, pp. 306-315.
7. I.W. Tsang, J.T. Kwok, and P.-M. Cheung, "Core vector machines: Fast SVM training on very large data sets," Journal of Machine Learning Research, vol. 6, pp. 363-392, 2005.
8. J.X. Dong, A. Krzyzak, and C.Y. Suen, "Fast SVM training algorithm with decomposition on very large datasets," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 603-618, April 2005.
9. G. Schohn and D. Cohn, "Less is more: Active learning with support vector machines," in Proc. 17th Int. Conf. Machine Learning, Stanford, CA, 2000.
10. O.L. Mangasarian, D.R. Musicant, "Lagrangian support vector machines, " Journal of Machine Learning Research, vol. 1, pp. 161-177, 2001.
11. G. Fung and O.L. Mangasarian,"Proximal support vector machine classifiers," in Proc. KDD-2001: Knowledge Discovery and Data Mining. F. Provost and R. Srikant, Ed. San Francisco, 2001, pp. 77-86, New York, Asscociation for Computing Machinery.
12. D.M.J. Tax and R.P.W. Duin, "Support vector data description," MachineLearing, vol. 54, no. 1 pp. 45-66, 2004.
13. C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," 2001.
14. KDD Cup 1999 Data. Available: http://www.ics.uci.edu/-kdd/databases/ kddcup99/kddcup99.html
15. L. Breiman, "Bias, variance, and arcing classifiers," Tec. Report 460, Statistics department, University of California, April 1996. Available: ftp://ftp.cs.toronto.edu/pub/ncuron/delve/data/tarfilcs/rinanorm. tar.gz
16. D.R. Musicant, "NDC: normally distributed clustered datasets," Computer Sciences Department, University of Wisconsin, Madison, 1998. Available: http://www.ca.wisc.edu/dmi/avm/ndc/