General polynomial time decomposition algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3559 LNAI, Issue , 2005, Pages 308-322

  List, Nikolas ^a   Simon, Hans Ulrich ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT THEORY; LEARNING SYSTEMS; OPTIMIZATION;

CONVEX QUADRATIC OPTIMIZATION; POLYNOMIAL TIME; POLYNOMIAL TIME DECOMPOSITION; SVM-OPTIMIZATION;

POLYNOMIALS;

EID: 26944489027     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11503415_21     Document Type: Conference Paper

References (30)

1. Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pages 144-152, 1992.
2. Steven Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
3. Chih-Chung Chang, Chih-Wei Hsu, and Chih-Jen Lin. The analysis of decomposition methods for support vector machines. IEEE Transactions on Neural Networks, 11(4):248-250, 2000.
4. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines, 2001. Available from http://www.csie.ntu.edu.tw/~cjlin/libsvm.
5. Pai-Hsuen Chen, Rong-En Fan, and Chih-Jen Lin. A study on SMO-type decomposition methods for support vector machines, 2005. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/generalSMO.pdf.
6. J. Dunn. Rates of convergence for conditional gradient algorithms near singular and non-singular extremals. SIAM J. Control and Optimization, 17(2):187-211, 1979.
7. Chih-Wei Hsu and Chih-Jen Lin. A simple decomposition method for support vector machines. Machine Learning, 46(1-3):291-314, 2002.
8. Don Hush and Clint Scovel. Polynomial-time decomposition algorithms for support vector machines. Machine Learning, 51(1):51-71, 2003.
9. Thorsten Joachims. Making large scale SVM learning practical. In Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-184. MIT Press, 1998.
10. S. Sathiya Keerthi and E. G. Gilbert. Convergence of a generalized SMO algorithm for SVM classifier design. Machine Learning, 46(1-3):351-360, 2002.
11. S. Sathiya Keerthi, Shirish Krishnaj Shevade, Chiranjib Bhattacharyya, and K. R. K. Murthy. Improvements to SMO algorithm for SVM regression. IEEE Transactions on Neural Networks, 11(5):1188-1193, 2000.
12. S. Sathiya Keerthi, Shirish Krishnaj Shevade, Chiranjib Bhattacharyya, and K. R. K. Murthy. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation, 13(3):637-649, 2001.
13. Pavel Laskov. Feasible direction decomposition algorithms for training support vector machines. Machine Learning, 46(1-3):315-349, 2002.
14. Shuo-Peng Liao, Hsuan-Tien Lin, and Chih-Jen Lin. A note on the decomposition methods for support vector regression. Neural Computation, 14(6):1267-1281, 2002.
15. Chih-Jen Lin. Linear convergence of a decomposition method for support vector machines, 2001. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/ linearconv.pdf.
16. Chih-Jen Lin. On the convergence of the decomposition method for support vector machines. IEEE Transactions on Neural Networks, 12(6):1288-1298, 2001.
17. Chih-Jen Lin. Asymptotic convergence of an SMO algorithm without any assumptions. IEEE Transactions on Neural Networks, 13(1):248-250, 2002.
18. Chih-Jen Lin. A formal analysis of stopping criteria of decomposition methods for support vector machines. IEEE Transactions on Neural Networks, 13(5):1045-1052, 2002.
19. Nikolas List. Convergence of a generalized gradient selection approach for the decomposition method. In Proceedings of the 15th International Conference on Algorithmic Learning Theory, pages 338-349, 2004.
20. Nikolas List and Hans Ulrich Simon. A general convergence theorem for the decomposition method. In Proceedings of the 17th Annual Conference on Computational Learning Theory, pages 363-377, 2004.
21. Olvi L. Mangasarian and David R. Musicant. Successive overrelaxation for support vector machines. IEEE Transactions on Neural Networks, 10(5): 1032-1037, 1999.
22. Olvi L. Mangasarian and David R. Musicant. Active support vector machine classification. In Advances in Neural Information Processing Systems 12, pages 577-583. MIT Press, 2000.
23. Olvi L. Mangasarian and David R. Musicant. Lagrangian support vector machines. Journal of Machine Learning Research, 1:161-177, 2001.
24. Nimrod Megiddo. Linear programming in linear time when the dimension is fixed. Journal of the Association on Computing Machinery, 31(1):114-127, 1984.
25. Edgar E. Osuna, Robert Freund, and Federico Girosi. Training support vector machines: an application to face detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 130-136, 1997.
26. John C. Platt. Fast training of support vector machines using sequential minimal optimization. In Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208. MIT Press, 1998.
27. Craig Saunders, Mark O. Stitson, Jason Weston, Leon Bottou, Bernhard Schölkopf, and Alexander J. Smola. Support vector machine reference manual. Technical Report CSD-TR-98-03, Royal Holloway, University of London, Egham, UK, 1998.
28. Hans Ulrich Simon. On the complexity of working set selection. In Proceedings of the 15th International Conference on Algorithmic Learning Theory, pages 324-337, 2004.
29. Vladimir Vapnik. Statistical Learning Theory. Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications, and Control. John Wiley & Sons, 1998.
30. S. V. N. Vishwanthan, Alexander J. Smola, and M. Narasimha Murty. SimpleSVM. In Proceedings of the 20th International Conference on Machine Learning, 2003.