A survey on training algorithms for support vector machine classifiers

Proceedings - 4th International Conference on Networked Computing and Advanced Information Management, NCM 2008

Volumn 1, Issue , 2008, Pages 123-128

  Wang, Guosheng ^a  

^a DEZHOU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; IMAGE RETRIEVAL; INFORMATION MANAGEMENT; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MANAGEMENT INFORMATION SYSTEMS; NEWTON-RAPHSON METHOD; SEMICONDUCTOR STORAGE; SUPPORT VECTOR MACHINES; SURVEYS; VECTORS;

APPLICATIONS.; CHALLENGING PROBLEMS; CLASSIFICATION TECHNIQUES; COMPUTATIONALLY EXPENSIVE; GENERALIZATION PERFORMANCES; MEMORY REQUIREMENTS; NEWTON METHODS; SUPPORT VECTOR MACHINE CLASSIFIERS; SUPPORT VECTORS; SVM TRAININGS; TRAINING ALGORITHMS; TRAINING EXAMPLES;

LEARNING ALGORITHMS;

EID: 57849150013     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NCM.2008.103     Document Type: Conference Paper

References (44)

1. R.Fletcher, Practical methods of Optimization, Wiley, 1988.
2. L. Mangasarian, Nonlinear Programming, SIAM, 1994.
3. M. Bazaraa, D. Sherali, and C. Shetty, Nonlinear programming: theory and algorithms, Wiley-Interscience Series in Discrete Mathematics and Optimization. Wiley, 1992.
4. E.Osuna, R.Freund, and F.Girosi, Training support vector machines: an application to face detection, Proceedings CVPR'97, 1997b.
5. C.Saunders, M.O.Stitson, J.Weston, L.Bottou, B. Schdieolkopf, and A.Smola, Support vector machine reference manual, Technical Report CSD-TR-98-03, Royal Holloway, University of London, Egham, UK, 1998.
6. T.Joachims, Making large-scale support vector machine learning practical, In Advances in Kernel Methods: Support Vector Machines, 1999.
7. C.J.Lin, On the convergence of the decomposition method for support vector machines, IEEE Transactions on Neural Networks, 12(6), 2001, 1288-1298.
8. light algorithm, Optimization Methods and Software, 20(2-3), 2005, 315-332.
9. J.Platt, Fast training of SVMs using sequential minimal optimization, In B. Schdieolkopf, C.Burges and A.Smola (ed.), Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, 1999, 185-208.
10. S.S.Keerthi, S.Shevade, C.Bhattacharyya and K.Murthy, Improvements to Platt's SMO algorithm for SVM classifier design, Tech Report, Dept. of CSA, Bangalore, India, 1999.
11. S.S.Keerthi, S.Shevade, C.Bhattacharyya and K.Murthy, Improvements to SMO algorithm for SVM regression, IEEE Transactions on Neural Networks 11(5), 2000, 1188-1193.
12. G.Flake, S.Lawrence, Efficient SVM regression training with SMO. Machine Learning, 46(1/3), 2002, 271-290.
13. S.S.Keerthi and E.G.Gilbert, Convergence of a generalized SMO algorithm for SVM classifier design, Machine Learning, 46, 2002, 351-360.
14. C.J.Lin, Asymptotic convergence of an SMO algorithm without any assumptions, IEEE Transaction on Neural Networks, 13(1), 2002, 248-250.
15. P.H.Chen, R.E.Fan and C.J.Lin, A study on SMO-Type decomposition method for support vector machines, Technical Report, Department of Computer Science, National Taiwan University, 2005.
16. R.E.Fan, P.H.Chen, and C.J.Lin, Working set selection using the second order information for training SVM, Journal of Machine Learning Research, 6, 2005, 1889-1918.
17. D. DeCoste and K. Wagstaff, Alpha seeding for support vector machines, In Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD-2000), 2000.
18. C.W.Hsu and C.J.Lin, A simple decomposition method for support vector machines, Machine Learning, 46, 2002, 291-314.
19. O.L.Mangasarian and D.R.Musicant, Successive over-relaxation for support vector machines, IEEE Transactions on Neural Networks, 10(5), 1999, 1032-1037.
20. C.C.Chang and C.J.Lin, LIBSVM: Software available at http://www.csie.ntu. edu.tw/̃cjlin/libsvm.
21. S.S.Keerthi and C.-J. Lin, Asymptotic behaviors of support vector machines with Gaussian kernel, Neural Computation, 15, 2003, 1667-1689.
22. J.Suykens and J.Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters, 1999, 293-300.
23. J.Suykens. L.Lukas, P.Van Dooren, B.De Moor and J.Vandewalle, Least squares support vector machine classifiers: a large scale algorithm, in Proc. of the European Conference on Circuit Theory and Design, Stresa, Italy, Sep., 1999, 893-842.
24. B.Hamers, J.Suykens and B.De Moor, A comparison of iterative methods for least squares support vector machine classifiers, Internal Report 01-110, ESAT-SISTA, K.U.Leuven (Leuven, Belgium), 2001.
25. W.Chu, C. J. Ong and S. S. Keerthi, An improved conjugate gradient scheme to the solution of least squares SVM, IEEE Transactions on Neural Networks 16(2) 2005, 498-501.
26. S.S.Keerthi and S.K.Shevade, SMO algorithm for least squares svm formulations, Neural Computation, 15(2), 2003, 487-507.
27. O.L.Mangasarian and D.R.Musicant, Lagrangian support vector machines, Journal of Machine Learning Research, 1, 2001, 161-177.
28. Glenn Fung and L.Mangasarian, Proximal support vector machine classifiers, Proceedings of the 7th ACM conference on knowledge discovery and data mining, ACM, 2001, 77-86.
29. Y.J.Lee and O.L.Mangasarian, RSVM: Reduced support vector machines, In Proceedings of the First SIAM International Conference on Data Mining, 2001.
30. Kuan-Ming Lin and Chih-Jen Lin, A study on reduced support vector machines, IEEE Transaction on Neural Networks, 14(6), 2003, 1449-1459.
31. S.S.Keerthi, S.K.Shevade, C.Bhattacharyya, and K.R.K., Murthy, A fast iterative nearest point algorithm for support vector machine classifier design, IEEE Transactions on neural networks, 11(1), 2000, 124-136.
32. Gilbert E. G., Minimizing the quadratic form on a convex set, SIAM J. Contr., 4, 1966, 61-79.
33. B.F.Mitchell, V. F. Demyanov, V. N. Malozemov, Finding the point of a polyhedron closest to the origin, SIAM J. Contr., 1974, 12: 19-26.
34. C. Campbell, N. Cristianini and A.Smola, Query learning with large margin classifiers, Proceedings of the Seventeenth International Conference on Machine Learning, eds., Pat Langley, Morgan Kaufmann, 2000, 111-118.
35. Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou, Fast kernel classifiers with online and active learning, Journal of Machine Learning Research (JMLR), 6, 2005, 1579-1619.
36. Simon Tong, Daphne Koller, Support vector machine active learning with application to text classification, Journal of Machine Learning Research, 2, 2001, 45-66.
37. N. Syed, H. Liu and K. Sung, Incremental learning with support vector machines, Proceedings of the Workshop on Support Vector Machines at LJCAI-99, Stockholm, Sweden, 1999.
38. G. Cauwenberghs and T. Poggio, Incremental and decremental support vector machine learning, In Advances in Neural Information Processing Systems (NIPS'2000), vol. 13, MIT Press, 2001, 409-415.
39. L. Csato and M. Opper, Sparse representation for Gaussian process models, In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13. MIT Press, 2001.
40. J. Kivinen, A. J. Smola, and R. C. Williamson, Online learning with kernels, IEEE Transactions on Signal Processing 52(8), 2004, 2165-2176.
41. O.L.Mangasarian, A finite Newton method for classification, Optimization Methods and Software, 17(5), 2002, 913-929.
42. Sathiya Keerthi and Dennis DeCoste, A modified finite Newton method for fast solution of large scale linear SVMs, Journal of Machine Learning Research. 6(5), 2005, 341-361.
43. T.-T.Friess, N.Cristianini, and C.Campbell, The kernel adatron algorithm: a fast and simple learning procedure for support vector machines, In Proceedings of 15th Intl. Conf Machine Learning, 1998.
44. Jose L.Balcazar, Yang Dai, and Osamu Watanabe, Provably fast training algorithms for support vector machines, Proceedings of IEEE International Conference on Data Mining, 2001, 43-50.