Parallel computation of RBF kernels for support vector classifiers

Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005

Volumn , Issue , 2005, Pages 334-345

  Qiu, Shibin ^a   Lane, Terran ^a  

^a MSC01 1070   (United States)

Author keywords

Kernel approximation; Parallel algorithms; Support vector machines; SVM performance

Indexed keywords

APPROXIMATION ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; DATA MINING; ITERATIVE METHODS; PARALLEL ALGORITHMS; RADIAL BASIS FUNCTION NETWORKS; SPACE DIVISION MULTIPLE ACCESS; SUPPORT VECTOR MACHINES;

CLASSIFICATION ALGORITHM; KERNEL APPROXIMATION; PARALLEL IMPLEMENTATIONS; POSITIVE DEFINITE KERNELS; SEQUENTIAL IMPLEMENTATION; SINGLE-PROCESSOR WORKSTATIONS; SUPPORT VECTOR CLASSIFIERS; SVM PERFORMANCE;

MATRIX ALGEBRA;

EID: 67549097518     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972757.30     Document Type: Conference Paper

References (28)

1. M. P. S. Brown, W. N. Grundy, D. Lin, N. Cristianini, C. W. Sugnet, T. S. Furey, Jr. M. Ares and D. Haussler, Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. USA, 97(2000), pp. 262-267.
2. A. Choudhury, P. B. Nair and A. J. Keane, A data parallel approach for large-scale Gaussian process modeling, In R. L. Grossman, J. Han, V. Kumar, H. Mannila and R. Motwani, Eds., Proc. 2nd SIAM Int'l Conf. Data Mining, Arlington, VA, USA, SIAM 2002.
3. R. Collobert, S. Bengio and Y. Bengio, A parallel mixture of SVMs for very large scale problems, Neural Computation, 14(5)(2002), pp. 1105-1114.
4. N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines, Cambridge University Press, 2000.
5. Thanh-Nghi Do and F. Poulet, Incremental SVM and visualizaiotn tools for bio-medical data mining, In Proc. European Workshop on Data Mining and Text Mining for Bioinformatics, Dubrovnik, Croatia, Sep., 2003.
6. fMRI data center, http://www.fmridc.org.
7. G. Fung and O. L. Mangasarian, Proximal support vector machine classifiers, Proc. 7th ACM Intl. Conf. KDD, San Francisco, USA, pp. 77-86, ACM, 2001.
8. G. Fung and O. L. Mangasarian, Incremental support vector machine classification, In R. Grossman, H. Mannila, R. Motwani, et al., Eds., Proc. 2nd SIAM Int'l Conf. Data Mining, pp. 247-260, SIAM 2002.
9. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization, Academic Press, 1981.
10. R. C. Houts and O. Alkin, Signal Analysis in Linear Systems, Saunder College Publishing, ISBN 0-03-028744-8.
11. T. Joachims, Making large scale SVM learning practical, In B. Scholkopf, C. Burges and A. J. Smola, Eds., Advances in Kernel Methods-Support Vector Learning, pp. 169-184, MIT Press, 1999.
12. T. Joachims, Text categorization with support vector machines: Learning with many relevant features, In C. Nedellec and C. Rouveirol, Eds., Proc. European Conf. Machine Learning, pp. 137-142, Berlin, Springer, 1998.
13. L. Kaufman, Solving the quadratic programming problem arising in support vector classification, In B. Scholkopf, C. Burges and A Smola, Eds., Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, USA, 1998.
14. Y.-L. Lee and O. L. Mangasarian, RSVM: Reduced support vector machines, In Proc. 1st SIAM Int'l Conf. Data Mining, Chicago, IL, April, SIAM, 2001.
15. Y.-L. Lee and O. L. Mangasarian, SSVM: A smooth support vector machine, Computational Optimization and Applications, 20(2001), pp. 5-22.
16. C. Leslie, E. Eskin and W. S. Noble, The spectrum kernel: a string kernel for SVM protein classification, In Proc. Pacific Symp. Biocomputing, pp. 566-575, PSB, 2002.
17. H. Lodhi, J. Shawe-Taylor, N. Cristianini, and C. Watkins, Text classification using string kernels, In T. K. Leen, T. G. Dietterich and V. Tresp, Eds., NIPS 2000, pp. 563-569, MIT Press, 2001.
18. P. Mahe, N. Ueda, T. Akutsu, Jean-Luc Perret and Jean-Philippe Vert, Extensions of marginalized graph kernels, In Proc. 21st Intl. Conf. Machine Learning, Banff, Canada, ICML, 2004.
19. O. L. Mangasarian, Generalized support vector machines, In A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, Eds., Advances in Large Margin Classifiers, pp. 135-146, Cambridge, MA, MIT Press, 2000.
20. L. Melton, Pharmacogenetics and Genotyping: On the trail of SNPs, Nature, 422(2003), pp. 917-923.
21. E. Osuna, R. Freund and F. Girosi, Support vector machines: training and applications, Tech. Report, AIM-1602, MIT, 1997.
22. J. Platt, Sequential minimal optimization: A fast algorithm for training support vector machines, In B. Scholkopf, C. Burges, and A Smola, Eds., Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, USA, 1998.
23. M. Reed and B. Simon, Methods of Modern Mathematical Physics I: Functional Analysis, 2nd edition, Academic Press, San Diego, CA, 1980.
24. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2001.
25. The thrombin dataset, http://www.cs.wisc.edu/~dpage/kddcup2001/.
26. A. Tveit and H. Engum, Parallelization of the incremental proximal support vector machine classifier using a heap-based tree topology, Tech. Report, IDI, NTNU, Trondheim, Norway, Aug. 2003.
27. UCI machine learning data datasets, http://www.ics.uci.edu/~mlearn/ MLRepository.html.
28. M. K. Warmuth, J. Liao, G. Ratsch, M. Mathieson, San-tosh Putta and C. Lemmen, Support vector machines for active learning in drug discovery process, Journal of Chemical Information Science, 43(2)(2003), pp. 667-673.