Abstract
This paper introduces a multistrategy learning approach to the cate- gorization of text documents. The approach benefits from two existing, and in our view complimentary, sets of categorization techniques: those based on Roc- chio’s algorithm and those belonging to the rule learning class of machine learning algorithms. Visualization is used for the presentation of the output of learning.
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References
Ittner, D. J., Lewis, D. D., and Ahn, D. D.: Text categorization of low quality images. In Symposium on Document Analysis and Information Retrieval. Las Vegas, NV. (1995) 301–315
Cohen, W. W.: Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California. (1995)
Cohen, W. W. and Singer, Y.: Context-sensitive learning methods for text categorization. In ACM Transactions on Information Systems 17, 2. (1999) 141–173
Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA. (1993)
Rocchio, J.: Relevance feedback information retrieval. In G. Salton, ed. The Smart retrieval system: experiments in automatic document processing. Prentice-Hall, Englewood Cliffs, NJ. (1971) 313–323.
Buckley, C., Salton, G., and Allan, J.: The effect of adding relevance information in a relevance feedback environment. In Proceedings of the 17th International ACM SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland. (1994) 292–300
Salton, G.: Developments in automatic text retrieval. Science 253. (1991) 974–980
Lewis, David D.: Homepage available at http://www.research.att.com/~lewis.
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Hadjarian, A., Bala, J., Pachowicz, P. (2001). Text Categorization through Multistrategy Learning and Visualization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2001. Lecture Notes in Computer Science, vol 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44686-9_42
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DOI: https://doi.org/10.1007/3-540-44686-9_42
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