SMO Algorithm for Least Squares SVM

Proceedings of the International Joint Conference on Neural Networks

Volumn 3, Issue , 2003, Pages 2088-2093

  Keerthi, S S ^a   Shevade, S K ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; LEAST SQUARES APPROXIMATIONS; VECTORS;

SUPPORT VECTOR MACHINES (SVM);

ALGORITHMS;

EID: 0141459748     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Chang, C.C., and Lin, C.J. (2001). LIBSVM: A library for support vector machines, Dept. of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
2. DeCoste, D., and Wagstaff, K. (2000). Alpha seeding for support vector machines, International Conference on Knowledge Discovery & Data Mining (KDD-2000).
3. Fletcher, R. (1987). Practical Methods of Optimization. John Wiley and Sons, 2nd Edition.
4. Hamers, B., Suykens, J., and De Moor, B. (2001). A comparison of iterative methods for least squares support vector machine classifiers, Internal Report 01-110, ESAT-SISTA, K.U.Leuven (Leuven, Belgium).
5. Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., and Murthy, K.R.K. (2001). Improvements to Plan's SMO algorithm for SVM design, Neural Computation, vol. 13, pp.637-649.
6. Keerthi, S.S., and Shevade, S.K. (2002). SMO Algorithm for Least Squares SVM Formulations, Technical Report CD-02-08, Control Division, Dept. of Mechanical Engineering, National University of Singapore, Singapore. Available at: http://guppy.mpe.nus.edu.sg/̃mpessk/papers/lssvm_smo.ps.gz
7. Mika, S., Rätsch, G., and Müller, K.R. (2001). A mathematical programming approach to the kernel Fisher algorithm, In T.K. Leen, T.G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pp.591-597, MIT Press.
8. Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines, Technical Report MSR-TR-98-14, Microsoft Research, Redmond.
9. th International Conference on Machine Learning ICML-98, Madison, WI, USA.
10. Smola, A., and Schölkopf, B. (1998) A tutorial on support vector regression, NeuroColt2 Technical Report NC2-TR-1998-030, ESPRIT Working Group in Neural and Computational Learning II.
11. Suykens, J., and Vandewalle, J. (1999). Least squares support vector machine classifiers, Neural Processing Letters, vol. 9, no. 3, June 1999, pp. 293-300.
12. Suykens, J., Lukas, L., Van Dooren, P., De Moor, B., and Vandewalle, J., (1999). Least squares support vector machine classifiers: a large scale algorithm, in Proc. of the European Conference on Circuit Theory and Design (ECCTD'99), Stresa, Italy, Sep. 1999, pp. 839-842.
13. Vanderbei, R.J. (1994). LOQO: An Interior Point Code for Quadratic Programming, Technical Report SOR-94-15, Statistics and Operations Research, Princeton University, NJ, USA.
14. Van Gestel, T., Suykens, J., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., De Moor, B., and Vandewalle, J. (2000). Benchmarking least squares support vector machine classifiers, Internal Report 00-37, ESAT-SISTA, K.U.Leuven (Leuven, Belgium), 2000.
15. Van Gestel, T., Suykens, J., Lanckriet, G., Lambrechts, A., De Moor, B., and Vandewalle, J. (2002). A Bayesian framework for least squares support vector machine classifiers, Neural Computation.