Estimation of mixture models using Co-EM

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3720 LNAI, Issue , 2005, Pages 35-46

  Bickel, Steffen ^a   Scheffer, Tobias ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PARAMETER ESTIMATION; RISK ASSESSMENT; WEBSITES;

CLUSTER ENTROPY; CLUSTERING; LIKELIHOOD; MIXTURE MODELS;

COMPUTER NETWORKS;

EID: 33646436173     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564096_9     Document Type: Conference Paper

References (22)

1. S. Abney. Bootstrapping. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002.
2. K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. Blei, and M. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3:1107-1135, 2002.
3. S. Becker and G. Hinton. A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355:161-163, 1992.
4. S. Bickel and T. Scheffer. Multi-view clustering. In Proceedings of the IEEE International Conference on Data Mining, 2004.
5. S. Bickel and T. Scheffer. Estimation of mixture models using Co-EM. In Proceedings of the ICML Workshop on Learning with Multiple Views, 2005.
6. D. Blei and M. Jordan. Modeling annotated data. In Proceedings of the ACM SIGIR Conference on Information Retrieval, 2003.
7. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the Conference on Computational Learning Theory, 1998.
8. U. Brefeld and T. Scheffer. Co-EM support vector learning. In Proceedings of the International Conference on Machine Learning, 2004.
9. M. Collins and Y. Singer. Unsupervised models for named entity classification. In Proc. of the Conf. on Empirical Methods in Natural Language Processing, 1999.
10. S. Dasgupta, M. Littman, and D. McAllester. PAC generalization bounds for cot-raining. In Proceedings of Neural Information Processing Systems, 2001.
11. V. de Sa. Learning classification with unlabeled data. In Proceedings of Neural Information Processing Systems, 1994.
12. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1977.
13. R. Ghani. Combining labeled and unlabeled data for multiclass text categorization. In Proceedings of the International Conference on Machine Learning, 2002.
14. K. Kailing, H. Kriegel, A. Pryakhin, and M. Schubert. Clustering multi-represented objects with noise. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2004.
15. A. McCallum and K. Nigam. Employing EM in pool-based active learning for text classification. In Proc. of the International Conference on Machine Learning, 1998.
16. I. Muslea, C. Kloblock, and S. Minton. Active + semi-supervised learning = robust multi-view learning. In Proc. of the International Conf. on Machine Learning, 2002.
17. K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of cotraining. In Proceedings of the Workshop on Information and Knowledge Management, 2000.
18. J. Sinkkonen, J. Nikkilä, L. Lahti, and S. Kaski. Associative clustering. In Proceedings of the European Conference on Machine Learning, 2004.
19. J. Wu. On the convergence properties of the EM algorithm. The Annals of Statistics, 11:95-103, 1983.
20. D. Yarowsky. Unsupervised word sense disambiguation rivaling supervised methods. In Proc. of the Annual Meeting of the Association for Comp. Ling., 1995.
21. Y. Zhao and G. Karypis. Criterion functions for document clustering: Experiments and analysis. Technical Report TR 01-40, Department of Computer Science, University of Minnesota, Minneapolis, MN, 2001, 2001.
22. Z. Zhou and M. Li. Semi-supervised regression with co-training. In Proceedings of the International Joint Conference on Artificial Intelligence, 2005.