Active learning using pre-clustering

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 623-630

  Nguyen, Hieu T ^a   Smeulders, Arnold ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; DATABASE SYSTEMS; INFORMATION ANALYSIS; INTEGRATION; LOGISTICS; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

ACTIVE LEARNING; CLASSIFICATION BOUNDARY; DATA REPRESENTATION; DISCRIMINATIVE MODELS;

LEARNING SYSTEMS;

EID: 14344265134     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Campbell, C., Cristianini, N., & Smola, A. (2000). Query learning with large margin classifiers. Proc. 17th International Conf. on Machine Learning (pp. 111-118). Morgan Kaufmann, CA.
2. Chapelle, O., Weston, J., & Scholkopf, B. (2002). Cluster kernels for semi-supervised learning. Advances in Neural Information Processing Systems.
3. Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. Journal of Artificial Intelligence research, 4, 129-145.
4. Kaufman, L., & Rousseeuw, P. (1990). Finding groups in data: An introduction to cluster analysis. John Wiley & Sons.
5. Lewis, D. D., & Gale, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval (pp. 3-12). Springer Verlag.
6. McCallum, A. K., & Nigam, K. (1998). Employing EM in pool-based active learning for text classification. Proc. 15th International Conf. on Machine Learning (pp. 350-358). Morgan Kaufmann, CA.
7. Miller, D., & Uyar, H. (1996). A mixture of experts classifier with learning based on both labelled and unlabelled data. Advances in Neural Information Processing Systems 9 (pp. 571-577).
8. Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103-134.
9. Pham, T., Worring, M., & Smeulders, A. (2002). Face detection by aggregated bayesian network classifiers. Pattern Recogn. Letters, 23, 451-461.
10. Roy, N., & McCallum, A. (2001). Toward optimal active learning through sampling estimation of error reduction. Proc. 18th International Conf. on Machine Learning (pp. 441-448). Morgan Kaufmann, CA.
11. Schohn, G., & Cohn, D. (2000). Less is more: Active learning with support vector machines. Proc. 17th International Conf. on Machine Learning (pp. 839-846). Morgan Kaufmann, CA.
12. Seeger, M. (2001). Learning with labeled and unlabeled data (Technical Report). Edinburgh University.
13. Shen, X., & Zhai, C. (2003). Active feedback - UIUC TREC-2003 HARD experiments. The 12th Text Retrieval Conference, TREC.
14. Struyf, A., Hubert, M., & Rousseeuw, P. (1997). Integrating robust clustering techniques in s-plus. Computational Statistics and Data Analysis, 26, 17-37.
15. Tang, M., Luo, X., & Roukos, S. (2002). Active learning for statistical natural language parsing. Proc. of the Association for Computational Linguistics 40th Anniversary Meeting. Philadelphia, PA.
16. Tong, S., & Chang, E. (2001). Support vector machine active learning for image retrieval. Proceedings of the 9th ACM int. conf. on Multimedia (pp. 107-118). Ottawa.
17. Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2, 45-66.
18. Xu, Z., Yu, K., Tresp, V., Xu, X., & Wang, J. (2003). Representative sampling for text classification using support vector machines. 25th European Conf. on Information Retrieval Research, ECIR 2003. Springer.
19. Zhang, C., & Chen, T. (2002). An active learning framework for content-based information retrieval. IEEE trans on multimedia, 4, 260-268.
20. Zhang, T., & Oles, F. (2000). A probability analysis on the value of unlabeled data for classification problems. Proc. Int. Conf. on Machine Learning.
21. Zhang, T., & Oles, F. J. (2001). Text categorization based on regularized linear classification methods. Information Retrieval, 4, 5-31.
22. Zhu, J., & Hastie, T. (2001). Kernel logistic regression and the import vector machine. Advances in Neural Information Processing Systems.