Improving semi-supervised support vector machines through unlabeled instances selection

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2011, Pages 386-391

  Li, Yu Feng ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; GENERALIZATION ABILITY; HIER-ARCHICAL CLUSTERING; LABELED DATA; LEARNING PERFORMANCE; SEMI-SUPERVISED; UNLABELED DATA;

ARTIFICIAL INTELLIGENCE;

SUPPORT VECTOR MACHINES;

EID: 80055041497     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. M. F. Balcan, A. Blum, P. P. Choi, J. Lafferty, B. Pantano, M. R. Rwebangira, and X. Zhu. Person identification in webcam images: An application of semi-supervised learning. In ICML Workshop on Learning with Partially Classified Training Data, 2005.
2. S. Ben-David, T. Lu, and D. Pal. Does unlabeled data provably help? Worst-case analysis of the sample complexity of semi-supervised learning. In COLT, pages 33-44, 2008.
3. K. Bennett and A. Demiriz. Semi-supervised support vector machines. In NIPS 11, pages 368-374. 1999.
4. A. Blum and S. Chawla. Learning from labeled and unlabeled data using graph mincuts. In ICML, pages 19-26, 2001.
5. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In COLT, pages 92-100, 1998.
6. O. Chapelle and A. Zien. Semi-supervised learning by low density separation. In AISTATS, pages 57-64, 2005.
7. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
8. O. Chapelle, V. Sindhwani, and S. S. Keerthi. Optimization techniques for semi-supervised support vector machines. J. Mach. Learn. Res., 9:203-233, 2008. (Pubitemid 351469022)
9. N. V. Chawla and G. Karakoulas. Learning from labeled and unlabeled data: An empirical study across techniques and domains. J. Artif. Intell. Res., 23:331-366, 2005. (Pubitemid 41525872)
10. R. Collobert, F. Sinz, J. Weston, and L. Bottou. Large scale transductive SVMs. J. Mach. Learn. Res., 7:1687-1712, 2006. (Pubitemid 44223088)
11. F. G. Cozman, I. Cohen, and M. C. Cirelo. Semi-supervised learning of mixture models. In ICML, pages 99-106, 2003.
12. S. Dasgupta, M. L. Littman, and D. McAllester. PAC generalization bounds for co-training. In NIPS 14, pages 375-382. 2002.
13. A.K. Jain and R.C. Dubes. Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ., 1988.
14. T. Jebara, J. Wang, and S. F. Chang. Graph construction and bmatching for semi-supervised learning. In ICML, pages 441-448, 2009.
15. T. Joachims. Transductive inference for text classification using support vector machines. In ICML, pages 200-209, 1999.
16. J. Lafferty and L. Wasserman. Statistical analysis of semi-supervised regression. In NIPS 20, pages 801-808. 2008.
17. M. Li and Z. H. Zhou. SETRED: Self-training with editing. In PAKDD, pages 611-621, 2005.
18. Y.-F. Li, J. T. Kwok, and Z.-H. Zhou. Semi-supervised learning using label mean. In ICML, pages 633-640, 2009.
19. D. J. Miller and H. S. Uyar. A mixture of experts classifier with learning based on both labelled and unlabelled data. In NIPS 9, pages 571-577. 1997.
20. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Mach. Learn., 39(2):103-134, 2000. (Pubitemid 30594822)
21. A. Singh, R. Nowak, and X. Zhu. Unlabeled data: Now it helps, now it doesn't. In NIPS 21, pages 1513-1520. 2009.
22. W. Wang and Z.-H. Zhou. Analyzing co-training style algorithms. In ECML, pages 454-465, 2007.
23. W. Wang and Z.-H. Zhou. A new analysis of co-training. In ICML, pages 1135-1142, 2010.
24. J. Wang, T. Jebara, and S. F. Chang. Graph transduction via alternating minimization. In ICML, pages 1144-1151, 2008.
25. T. Zhang and F. Oles. The value of unlabeled data for classification problems. In ICML, pages 1191-1198, 2000.
26. Z.-H. Zhou and M. Li. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Eng., 17(11):1529-1541, 2005. (Pubitemid 41704840)
27. Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowl. Inf. Syst., 24(3);415-439, 2010.
28. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Scholkopf. Learning with local and global consistency. In NIPS 16, pages 595-602. 2004.
29. X. Zhu, Z. Ghahramani, and J. D. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.
30. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Dept. Comp. Sci., Univ. Wisconsin-Madison, 2006.