Training sparse SVM on the core sets of fitting-planes

Neurocomputing

Volumn 130, Issue , 2014, Pages 20-27

  Li, Ziqiang ^a,b   Zhou, Mingtian ^a   Xu, LiJia ^b   Lin, Hao ^a   Pu, Haibo ^b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b SICHUAN AGRICULTURAL UNIVERSITY   (China)

Author keywords

Core set; Fitting plane; Sparsity; SVM

Indexed keywords

EID: 84893799172     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.04.046     Document Type: Article

References (22)

1. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20:273-297.
2. Fan R.-E., et al. Working set selection using second order information for training support vector machines. Journal of Machine Learning Research 2005, 6:1889-1918.
3. T. Joachims, Making large scale SVM learning practical, Advances in Kernel Methods-Support Vector Learning, 1998.
4. C.J.C. Burges, Simplified support vector decision rules, in: Proceedings of 13th International Conference on Machine Learning, 1996, p. 7.
5. Downs T., et al. Exact simplification of support vector solutions. Journal of Machine Learning Research 2002, 2:293-297.
6. Liang X., et al. Pruning support vector machines without altering performances. IEEE Transactions on Neural Networks 2008, 19:1792-1803.
7. A. Smola, B. Schölkopf, Sparse greedy matrix approximation for machine learning, Presented at the ICML, 2000.
8. F.R. Bach, M.I. Jordan, Predictive low-rank decomposition for kernel methods, in:Proceedings of ICML 2005: 22nd International Conference on Machine Learning, August 7, 2005, pp. 33-40.
9. Lin K.M., Lin C.J. A study on reduced support vector machines. IEEE Transactions on Neural Networks 2003, 14:1449-1459.
10. Lee Y.J., Huang S.Y. Reduced support vector machines: A statistical theory. IEEE Transactions on Neural Networks 2007, 18:1-13.
11. M. Wu, et al., Building sparse large margin classifiers, in: Proceedings of ICML 2005: 22nd International Conference on Machine Learning, August 7, 2005, pp. 1001-1008.
12. Keerthi S.S., et al. Building support vector machines with reduced classifier complexity. Journal of Machine Learning Research 2006, 7:1493-1515.
13. Joachims T., Yu C.N.J. Sparse kernel SVMs via cutting-plane training. Machine Learning 2009, 76:179-193.
14. Sun P., Yao X. Sparse approximation through boosting for learning large scale Kernel machines. IEEE Transactions on Neural Networks 2010, 21:883-894.
15. Peng X.J. Building sparse twin support vector machine classifiers in primal space. Information Sciences 2011, 181:3967-3980.
16. Tsang I.W.H., et al. Generalized core vector machines. IEEE Transactions on Neural Networks 2006, 17:1126-1140.
17. Licheng J., et al. Fast sparse approximation for least squares support vector machine. IEEE Transactions on Neural Networks 2007, 18:685-697.
18. B. Scholkopf, et al., A generalized representer theorem, in: Proceedings of Computational Learning Theory, vol. 2111, 2001, pp. 416-426.
19. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999, 9:293-300.
20. Jayadeva, et al. Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29:905-910.
21. Badoiu M., Clarkson K.L. Optimal core-sets for balls. Computational Geometry-Theory and Applications 2008, 40:14-22.
22. I.W. Tsang, A. Kocsor, et al. Simpler core vector machines with enclosing balls, in: Proceedings of 24th International Conference on Machine Learning, ICML 2007, vol. 227, 2007, pp. 911-918.