Automatic feature decomposition for single view co-training

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 953-960

  Chen, Minmin ^a   Weinberger, Kilian Q ^a   Chen, Yixin ^a  

^a WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CO-TRAINING; FEATURE DECOMPOSITION; FEATURE SPACE; LEARNING SCENARIOS; MULTI-VIEWS; NOVEL ALGORITHM; SEMI-SUPERVISED LEARNING; TRAINING SETS; UNLABELED DATA;

ALGORITHMS; OBJECT RECOGNITION; SUPERVISED LEARNING;

TEACHING;

EID: 80053456516     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Abney, S. Bootstrapping. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 360-367, 2002.
2. Argyriou, A., Evgeniou, T., and Pontil, M. Multi-task feature learning. Advances in neural information processing systems, 19:41.
3. Balcan, M.F., Blum, A., and Yang, K. Co-training and expansion: Towards bridging theory and practice. Advances in neural information processing systems, 17:89-96, 2004.
4. Bergamo, A. and Torresani, L. Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach. In Neural Information Processing Systems (NIPS), 2010.
5. Bertsekas, D.P., Hager, W.W., and Mangasarian, O.L. Nonlinear programming. Athena Scientific Belmont, MA, 1999.
6. Blum, A. and Mitchell, T. Combining labeled and unlabeled data with co-training. In Proceedings of the eleventh annual conference on Computational learning theory, pp. 100. ACM, 1998.
7. Brefeld, U. and Scheffer, T. Co-EM support vector learning. In Proceedings of the twenty-first international conference on Machine learning, pp. 16. ACM, 2004.
8. Chan, J., Koprinska, I., and Poon, J. Co-training with a single natural feature set applied to email classification. In Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 586-589, 2004.
9. Collins, M. and Singer, Y. Unsupervised models for named entity classification. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 189-196, 1999.
10. Fergus, R., Fei-Fei, L., Perona, P., and Zisserman, A. Learning Object Categories from Google's Image Search. Computer Vision, Tenth IEEE International Conference on, 2, 2005.
11. Ghani, R. Combining labeled and unlabeled data for text classification with a large number of categories. In Proceedings of the IEEE International Conference on Data Mining, volume 2, 2001.
12. Griffin, G., Holub, A., and Perona, P. Caltech-256 object category dataset. 2007.
13. Hyvárinen, A., Hurri, J., and Hoyer, P.O. Independent component analysis. Natural Image Statistics, pp. 151-175.
14. Joachims, T. Transductive inference for text classification using support vector machines. In Machine Learning International Workshop, pp. 200-209. Citeseer, 1999.
15. Levin, A., Viola, P., and Freund, Y. Unsupervised improvement of visual detectors using co-training. In Proc. ICCV, volume 2, pp. 626-633. Citeseer, 2003.
16. Lorenzo, T., Szummer, M., and Fitzgibbon, A. Efficient object category recognition using classemes. In European Conference on Computer Vision (ECCV), pp. 776-789, September 2010.
17. Ng, A.Y. and Jordan, M.I. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, 2:841-848, 2002.
18. Nigam, K. and Ghani, R. Analyzing the effectiveness and applicability of co-training. In Proceedings of the ninth international conference on Information and knowledge management, pp. 86-93. ACM, 2000.
19. Obozinski, G., Taskar, B., and Jordan, M. Multi-task feature selection. In the workshop of structural Knowledge Transfer for Machine Learning in the 23rd International Conference on Machine Learning (ICML 2006). Citeseer, 2006.
20. Vijayanarasimhan, S. and Grauman, K. Keywords to visual categories: Multiple-instance learning forweakly supervised object categorization. 2008.
21. Zhang, W. and Zheng, Q. TSFS: A Novel Algorithm for Single View Co-training. In Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on, volume 1, pp. 492-496, 2009.
22. Zhu, X. Semi-supervised learning literature survey. Computer Science, University of Wisconsin-Madison, 2006.