Abstract
The most common model of machine learning algorithms involves two life-stages, namely the learning stage and the application stage. The cost of human expertise makes difficult the labeling of large sets of data for the training of machine learning algorithms. In this paper, we propose to challenge this strict dichotomy in the life cycle while addressing the issue of labeling of data. We discuss a learning paradigm called Continuous Learning. After an initial training based on human-labeled data, a Continuously Learning algorithm iteratively trains itself with the result of its own previous application stage and without the privilege of any external feedback. The intuitive motivation and idea of this paradigm are elucidated, followed by explanations on how it differs from other learning models. Finally, empirical evaluation of Continuous Learning applied to the Naive Bayesian Classifier for the classification of newsgroup articles of a well-known benchmark is presented.
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References
Esposito, F., Malerba, D., Semeraro, G., Fanizzi, N., Ferili, S.: Adding Machine Learning and Knowledge Intensive Techniques to a Digital Library Service. International Journal on Digital Libraries 2(1), 3–19 (1998)
Kupiec, J., Pederson, J., Chen, F.: A trainable document summarizer. In: Proceedings of SIGIR 1995, pp. 68–73. ACM Press, New York (1995)
Bartell, B.T., Cottrell, G.W., Belew, R.K.: Learning the optimal parameters in a ranked retrieval system using multi-query relevance feedback. In: Proceedings Symposium on Document Analysis and Information Retrieval (1994)
Vega, V.B., Bressan, S.: Indexing the Indonesian Web: Language Identification and Miscellaneous Issues. In: Poster Proceedings of 10th World Wide Web Conference (2001)
Vega, V.B., Bressan, S.: Continuous-Learning Weighted-Trigram Approach for Indonesian Language Distinction: A Preliminary Study. In: Proceedings of 19th International Conference on Computer Processing of Oriental Languages (2001)
Blum, A., Tom Mitchell, T.: Combining Labeled and Unlabeled Data with Co- Training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory (1998)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning 39(2/3), 103–134 (2000)
Smyth, P.: Learning with Probabilistic Supervision. In: Petsche, T., Hanson, S., Shavlik, J. (eds.) Computational Learning Theory and Natural Learning Systems, vol. 3, pp. 163–182. MIT Press, Cambridge (1995)
Kearns, M., Li, M.: Learning in the presence of malicious errors. SIAM Journal on Computing 22(4), 807–837 (1993)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B 39, 1–38 (1997)
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Vega S. N., V.B., Bressan, S. (2003). Continuous Naive Bayesian Classifications. In: Sembok, T.M.T., Zaman, H.B., Chen, H., Urs, S.R., Myaeng, SH. (eds) Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access. ICADL 2003. Lecture Notes in Computer Science, vol 2911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24594-0_27
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DOI: https://doi.org/10.1007/978-3-540-24594-0_27
Publisher Name: Springer, Berlin, Heidelberg
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