참고문헌
- Lewis D., and Gale, W., 'A sequential algorithm for training text classifiers,' In Proceedings of the 17th ACM-SIGIR Conference, pp. 3-12, 1994
- Roy N. and McCallum, A., 'Toward optimal active learning through sampling estimation of error reduction,' In Proceedings of the 18th International Conference on Machine Learning, pp. 441 -448, 200l
- Brinker, K., 'Incorporating Diversity in Active Learning with Support Vector Machines,' In Proceedings of 20th International Conference on Machine Learning, pp. 59-66, 2003
- UCI Knowledge Discovery in Databases Archive, http://kdd.ics.uci.edu/
- Basu, S., Banerjee, A., and Mooney, R., 'Semi-supervised clustering by seeding,' In Proceedings of the 19th International Conference on Machine Learning, pp. 19-26, 2002
- Yang, Y., 'An evaluation of statistical approaches to text categorization,' Journal of Information Retrieval, Vol. 1, Nos. 1/2, pp. 67-88, 1999 https://doi.org/10.1023/A:1009982220290
- Yates, B. and Neto, R., Modem Information Retrieval, Addison-Wesley, 1999
- Seung, H. S., Opper, M. and Sompolinsky, H., 'Query by committee,' In Computational Learing Theory, pp. 287-294, 1992
- Freund, Y., Seung, H. S., Shamir, E. and Tishby, N., 'Selective sampling using the query by committee algorithm,' Machine Learning, Vol. 28, Nos. 2-3, pp. 133-168, 1997 https://doi.org/10.1023/A:1007330508534
- Abe, N., and Mamitsuka, H. 'Querying learning using boosting and bagging,' In Proceedings of International Conference on Machine Learning, pp. 1-10, 1998
- Muslea, I., Minton, S. and Knoblock. C, 'Selective sampling with redundant views,' In Proceedings National Conference on Artificial Intelligence, pp. 621-626, 2000
- Blum A. and Mitchell, T., 'Combining labeled and unlabeled data with co-training,' In COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers, pp. 92-100, 1998 https://doi.org/10.1145/279943.279962
- Nigam, K., and Ghani, R. 'Analyzing the effectiveness and applicability of co-training,' In Proceedings of Information and Knowledge Management, pp. 86-93, 2000 https://doi.org/10.1145/354756.354805
- Muslea, I., Minton, S., Knoblock, C., 'Active + Semi-Supervised Learning = Robust Multi-View Learning,' In Proceedings of the 19th International Conference on Machine Learning, pp. 435-442, 2002
- Cohn, D., Ghahramani, Z., Jordan, M. I., 'Active learning with statistical models,' Journal of Artificial Intelligence Research, Vol. 4, pp. 129-145, 1996
- McCallum, A., and Nigam, K., 'Employing EM in pool-based active learning for text classification,' In Proceedings of the 15th International Conference on Machine Learning, pp. 359-367, 1998
- Plutowski, M. and White, H. 'Selecting Concise Training Sets from Clean Data,' IEEE Trans. Neural Networks, Vol. 4, No.2, pp. 305-318, 1993 https://doi.org/10.1109/72.207618
- Jung, G. and Opper, M. 'Selection of examples for a linear classifier,' Journal of Physics A, 29, pp. 1367-1380, 1996 https://doi.org/10.1088/0305-4470/29/7/010
- Mitra, P. Murthy, C.A. and Pal, S. K. 'Density Based Multiscale Data Condensation,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 6, pp. 734-747, 2002 https://doi.org/10.1109/TPAMI.2002.1008381
- Provost, F., and Kolluri, V., 'A survey of methods for scaling up inductive algorithms,' Data Mining Knowledge Discuvery, Vol. 2, pp. 131-169, 1999 https://doi.org/10.1023/A:1009876119989
- Shih, L., Rennie, J. D. M., Chang, Y.-H., and Karger, D. R., 'Text Bundling: Statistics-Based Data Reduction,' In Proceedings of the 20th International Coriference on Machine Learning, pp, 696-703, 2003