A sparse multi-class Least-Squares Support Vector Machine

IEEE International Symposium on Industrial Electronics

Volumn , Issue , 2008, Pages 1230-1235

  Xia, Xiao Lei ^a   Li, Kang ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; COMPUTATIONAL FLUID DYNAMICS; IMAGE RETRIEVAL; INDUSTRIAL ELECTRONICS; LEARNING SYSTEMS; RESEARCH AIRCRAFT; TWO PHASE FLOW; VECTORS;

BINARY CLASSIFIERS; GENERALIZATION CAPACITIES; PAPER ADDRESSES; REGRESSION ALGORITHMS; SPARSENESS PROBLEMS; SUPPORT VECTORS; SVM CLASSIFICATIONS; SVM CLASSIFIERS; SVM METHODS; SYNTHETIC DATUMS; TEST EXAMPLES; TRAINING SAMPLES; WEIGHT COEFFICIENTS;

SUPPORT VECTOR MACHINES;

EID: 57849085258     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIE.2008.4676989     Document Type: Conference Paper

References (13)

1. C. J. C. Burges. Simplified support vector decision rules. In L. Saitta, editor, Proc. of Proc. 13th Int'l Corf. Machine Learning, pages 71-77, San Mateo, CA, 1996.
2. T. Downs, K. E. Gates, and A. Masters. Exact simplification of support vector solutions. J. Much. Learn. Res., 12:293-297, 2001.
3. R. Fletcher. Practical Methods of Optimization. John Wiley and Sons, Chichester and New York, 1987.
4. T. V. Gestel, J. A. K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. D. Moor, and J. Vandewalle. Benchmarking least squares support vector machine classifiers. Machine Learning, 54(1):5-32, 2004.
5. K. Li, J.-X. Peng, and E. Bai. A two-stage algorithm for identification of nonlinear dynamic systems. Automatica, 42(7): 1189-1197, 2006.
6. K. Li, J.-X. Peng, and G. W. Irwin. A fast nonlinear model identification method. IEEE Trans. Automat. Control., 50(8):]211-1216, 2005.
7. O. L. Mangasarian and D. R. Musicant. Successive overrelaxation for support vector machines. IEEE Transactions on Neural Networks, 10:1032-1037, 1999.
8. K. R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2): 181-202, 2001.
9. B. Schölkopf, S. Mika, C. J. C. Burges, P. Knirsch, K. Miiller, G. Gitsch, and A. J. Smola. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5): 1000-1017, 1999.
10. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
11. J. A. K. Suykens and J. Vandewalle. Least squares support vector machine classifiers. Neural Processing Letters, 9(3):293-300, 1999.
12. J. A. K. Suykens and J. Vandewalle. Multiclass least squares support vector machines. volume 2, pages 900-903, Washington, DC, USA, 1999.
13. K. L. Xiao Lei (Celina) Xia and G. W. Irwin. Improved training of an optimal sparse least squares support vector machine. (accepted). 17th IFAC World Congress, July 6-11, 2008, Seoul, Korea.