Semi-Supervised Learning
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  2. Abney, S. P. (2007). Semisupervised learning for computational linguistics: Chapman & Hall/CRC.
  3. Bennett, K. P., & Demiriz, A. (1998). Semi-supervised support vector machines. Paper presented at the Proceedings of the 1998 conference on Advances in neural information processing systems.
  4. Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. Paper presented at the Proceedings of the 11th Annual Conference on Computational Learning Theory.
  5. Cao, Y., Li, H., & Lian, L. (2003). Uncertainty reduction in collaborative bootstrapping: measure and algorithm. Paper presented at the Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, Sapporo, Japan.
  6. Chapelle, O., Scholkopf, B., & Zien, A. (2006). Semi-supervised Learning: The MIT Press.
  7. Chapelle, O., Weston, J., & Scholkopf, B. (2003). Cluster kernels for semi-supervised learning. In S. T. S. Becker, and K. Obermayer (Ed.), Advances in Neural Information Processing Systems (Vol. 15, pp. 585-592): MIT Press.
  8. Collins, M., & Singer, Y. (1999). Unsupervised models for named entity classification. Paper presented at the Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora.
  9. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39, 1-38.
  10. Feng, H., & Chua, T.-S. (2003). A bootstrapping approach to annotating large image collection. Paper presented at the Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval, Berkeley, California.
  11. Goldman, S., & Zhou, Y. (2000). Enhancing supervised learning with unlabeled data. Paper presented at the Proceedings of the 17th International Conference on Machine Learning, San Francisco, CA.
  12. Goutte, C., Dejean, H., Gaussier, E., Cancedda, N., & Renders, J.-M. (2002). Combining labelled and unlabelled data: A case study on fisher kernels and transductive inference for biological entity recognition. Paper presented at the Proceedings of the 6th Conference on Natural Language Learning.
  13. Joachims, T. (1999). Transductive inference for text classification using support vector machines. Paper presented at the Proceedings of the International Conference on Machine Learning.
  14. Li, K., Zhang, W., Ma, X., Cao, Z., & Zhang, C. (2008). A novel semi-supervised SVM based on tri-training. IEEE Computer Society, 3, 47-51.
  15. Maeireizo, B., Litman, D., & Hwa, R. (2004). Co-training for predicting emotions with spoken dialogue data. Paper presented at the Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics.
  16. Nigam, K., & Ghani, R. (2000). Analyzing the effectiveness and applicability of co-training. Paper presented at the Proceedings of the Ninth International Conference on Information and Knowledge Management
  17. Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (1999). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103-134.
  18. Pierce, D., & Cardie, C. (2001). Limitations of co-training for natural language learning from large datasets. Paper presented at the Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing,Pittsburgh, PA.
  19. Suzuki, Y., Takamura, H., & Okumura, M. (2006). Application of semi-supervised learning to evaluative expression classification. Paper presented at the Seventh international conference on Computational linguistics and intelligent text processing (CICLING).
  20. Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2, 45-66.
  21. Wang, W., & Zhou, Z.-H. (2007). Analyzing co-training style algorithms. Paper presented at the Proceedings of the 18th European Conference on Machine Learning.
  22. Zhang, Z. (2004). Weakly-supervised relation classification for information extraction. Paper presented at the Proceedings of the 13th Conference of Information and Knowledge Management, Washington, DC.
  23. Zhou, Z.-H., & Li, M. (2005). Semi-supervised regression with co-training. Paper presented at the Proceedings of the 19th International Joint Conference on Artificail Intelligence, Edinburgh, Scotland.
  24. Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529-1541.
  25. Zhu, X. (2008). Semi-supervised learning literature survey: Department of Computer Sciences, University of Wisconsin, Madisono. Document Number)
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