Co-training with insufficient views

Journal of Machine Learning Research

Volumn 29, Issue , 2013, Pages 467-482

  Wang, Wei ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Co training; Insufficient views

Indexed keywords

CO-TRAINING; INSUFFICIENT VIEWS;

EID: 84908488054     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (22)

1. S. Abney. Bootstrapping. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 360-367, Philadelphia, PA, 2002.
2. R. K. Ando and T. Zhang. Two-view feature generation model for semi-supervised learning. In Proceedings of the the 24th International Conference on Machine Learning, pages 25- 32, Corvallis, OR, 2007.
3. M. Anthony and P. Bartlett, editors. Neural Network Learning: Theoretical Foundations. Cambridge University Press, Cambridge, 1999.
4. M.-F. Balcan and A. Blum. A discriminative model for semi-supervised learning. Journal of the ACM, 57(3), 2010.
5. M.-F. Balcan, A. Blum, and K. Yang. Co-training and expansion: Towards bridging theory and practice. In Advances in Neural Information Processing Systems 17, pages 89-96. MIT Press, Cambridge, MA, 2005.
6. 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 Proceedings of the 21st Annual Conference on Learning Theory, pages 33-44, Helsinki, Finland, 2008.
7. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Computational Learning Theory, pages 92-100, Madison, WI, 1998.
8. U. Brefeld, T. Gärtner, T. Scheffer, and S. Wrobel. Efficient co-regularised least squares regression. In Proceedings of the the 23th International Conference on Machine Learning, pages 137-144, Pittsburgh, PA, 2006.
9. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, 2006.
10. S. Dasgupta, M. Littman, and D. McAllester. PAC generalization bounds for co-training. In Advances in Neural Information Processing Systems 14, pages 375-382. MIT Press, Cambridge, MA, 2002.
11. L. Devroye, L. Györfi, and G. Lugosi, editors. A Probabilistic Theory of Pattern Recognition. Springer, New York, 1996.
12. J. Farquhar, D. R. Hardoon, H. Meng, J. Shawe-Taylor, and S. Szedmák. Two view learning: Svm-2k, theory and practice. In Advances in Neural Information Processing Systems 18. MIT Press, Cambridge, MA, 2006.
13. R. Hwa, M. Osborne, A. Sarkar, and M. Steedman. Corrected co-training for statistical parsers. In Working Notes of the ICML'03 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, Washington, DC, 2003.
14. V. Sindhwani, P. Niyogi, and M. Belkin. A co-regularization approach to semi-supervised learning with multiple views. In Working Notes of the ICML'05 Workshop on Learning with Multiple Views, Bonn, Germany, 2005.
15. K. Sridharan and S. M. Kakade. An information theoretic framework for multi-view learning. In Proceedings of the 21st Annual Conference on Learning Theory, pages 403-414, Helsinki, Finland, 2008.
16. M. Steedman, M. Osborne, A. Sarkar, S. Clark, R. Hwa, J. Hockenmaier, P. Ruhlen, S. Baker, and J. Crim. Bootstrapping statistical parsers from small data sets. In Proceedings of the 11th Conference on the European Chapter of the Association for Computational Linguistics, pages 331-338, Budapest, Hungary, 2003.
17. A. Tsybakov. Optimal aggregation of classifiers in statistical learning. The Annals of Statistics, 32(1):135-166, 2004.
18. W. Wang and Z.-H. Zhou. Analyzing co-training style algorithms. In Proceedings of the 18th European Conference on Machine Learning, pages 454-465, Warsaw, Poland, 2007.
19. W. Wang and Z.-H. Zhou. On multi-view active learning and the combination with semisupervised learning. In Proceedings of the 25th International Conference on Machine Learning, pages 1152-1159, Helsinki, Finland, 2008.
20. Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowledge and Information Systems, 24(3):415-439, 2010.
21. Z.-H. Zhou, K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 24(2):219-244, 2006.
22. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Department of Computer Sciences, University of Wisconsin at Madison, 2007.