Safety-aware semi-supervised classification

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 11, 2013, Pages 1763-1772

  Wang, Yunyun ^a   Chen, Songcan ^b  

^a NANJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

Alternating iterative strategy; least square support vector machine (LS SVM); semi supervised classification; semi supervised classification based on class memberships (SSCCM)

Indexed keywords

ADAPTIVE TRADEOFF; ITERATIVE STRATEGY; LEAST-SQUARE SUPPORT VECTOR MACHINES; OPTIMIZATION PROBLEMS; REAL APPLICATIONS; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED CLASSIFICATION METHOD; SUPERVISED CLASSIFICATION;

ITERATIVE METHODS; SUPPORT VECTOR MACHINES;

SUPERVISED LEARNING;

EID: 84886946460     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2263512     Document Type: Article

References (24)

1. Z.-H. Zhou and M. Li, "Semi-supervised learning by disagreement," Knowl. Inf. Syst., vol. 24, no. 3, pp. 415-439, 2010
2. X. Zhu and A. B. Goldberg, Introduction to Semi-Supervised Learning. San Mateo, CA, USA: Morgan Kaufmann, 2009
3. X. Zhu, "Semi-supervised learning literature survey," Ph.D. dissertation, Dept. Comput. Sci., Wisconsin-Madison, Univ., Madison, WI, USA, Jul. 2008
4. O. Chapelle, B. Scholkopf, and A. Zien, Semi-Supervised Learning. Cambridge, MA, USA: MIT Press, 2006
5. P. K. Mallapragada, R. Jin, A. K. Jain, and Y. Liu, "Semi-boost: Boosting for semi-supervised learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 11, pp. 2000-2014, Nov. 2009
6. T. Joachims, "Transductive inference for text classification using support vector machines," in Proc. 16th Int. Conf. Mach. Learn., 1999, pp. 200-209
7. G. Fung and O. L. Mangasarian, "Semi-supervised support vector machine for unlabeled data classification," Optim. Methods Softw., vol. 15, no. 1, pp. 99-105, 2001
8. R. Collobert, F. Sinz, J. Weston, and L. Bottou, "Large scale transductive SVMs," J. Mach. Learn. Res., vol. 7, pp. 1687-1712, Jan. 2006 (Pubitemid 44223088)
9. Y.-F. Li, J. Kwok, and Z.-H. Zhou, "Semi-supervised learning using label mean," in Proc. 26th Int. Conf. Mach. Learn., 2009, pp. 633-640
10. Y. Bengio, O. B. Alleau, and N. L. Roux, "Label propagation and quadratic criterion," in Semi-Supervised Learning, Cambridge, MA, USA: MIT Press, 2006, pp. 193-216
11. X. Zhu and Z. Ghahramani, "Learning from labeled and unlabeled data with label propagation," Dept. Lang. Technol., Carnegie Mellon Univ., Pittsburgh, PA, USA, Tech. Rep. CMU-CALD-02-107, 2002
12. A. Blum and S. Chawla, "Learning from labeled and unlabeled data using graph mincuts," in Proc. 18th Int. Conf. Mach. Learn., 2001, pp. 19-26
13. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples," J. Mach. Learn. Res., vol. 7, no. 1, pp. 2399-2434, 2006 (Pubitemid 44708005)
14. B. Pfahringer, "A semi-supervised spam mail detector," in Proc. 17th Eur. Conf. Mach. Learn. 10th Eur. Conf. Principles Pract. Knowl. Discovery Databases, 2006, pp. 1-5
15. N. Kasabov and S. Pang, "Transductive support vector machines and applications in bioinformatics for promoter recognition," Neural Inf. Process. Lett. Rev., vol. 3, pp. 31-38, May 2004
16. T. Yang and C. E. Priebe, "The effect of model misspecification on semi-supervised classification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 10, pp. 2093-2103, Oct. 2011
17. Y.-F. Li and Z.-H. Zhou, "Towards making unlabeled data never hurt," in Proc. 28th Int. Conf. Mach. Learn., 2011, pp. 1081-1088
18. Y.-F. Li and Z.-H. Zhou, "Improving semi-supervised support vector machines through unlabeled instances selection," in Proc. 25th AAAI Conf. Artif. Intell., 2011, pp. 500-505
19. Y. Wang, S. Chen, and Z.-H. Zhou, "New semi-supervised classification method based on modified cluster assumption," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 5, pp. 689-702, May 2012
20. B. Yang and S. Chen, "Disguised discrimination of locality-based unsupervised dimensionality reduction," Int. J. Pattern Recognit. Artif. Intell., vol. 24, no. 7, pp. 1011-1025, 2010
21. J. Gorski and F. Pfeuffer, "Biconvex sets and optimization with biconvex functions: A survey and extensions," Math. Methods Oper. Res., vol. 66, no. 3, pp. 373-407, 2007 (Pubitemid 350159777)
22. Z. Xu, R. Jin, I. King, M. R. Lyu, and Z. Yang, "Adaptive regularization for transductive support vector machine," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 2125-2133
23. K. Chen and S. Wang, "Regularized boost for semi-supervised learning," in Proc. Adv. Neural Inf. Process. Syst., 2008, pp. 281-288
24. M. Gonen and E. Alpaydin, "Multiple kernel learning algorithms," J. Mach. Learn. Res., vol. 12, pp. 2211-2268, Jan. 2011.