1D. D. Lewis. Naive (Bayes) at forty: The independence assumption in information retrieval. In: Proc. of the 10th European Conf. on Machine Learning. New York: Springer,1998, 4-15.
2Y. Yang, X. Lin. A re-examination of text categorization methods. In: The 22nd Annual Int'l ACM SIGIR Conf. onResearch and Development in the Information Retrieval. NewYork: ACM Press, 1999.
3Y. Yang, C. G. Chute. An example based mapping method for text categorization and retrieval. ACM Trans. on Information Systems, 1994, 12(3): 252 -277.
4E. Wiener. A neural network approach to topic spotting. The 4th Annual Syrup. on Document Analysis and Information Retrieval,Las Vegas, NV, 1995.
5R. E. Schapire, Y. Singer. Improved boosting algorithms using confidence-rated predications. In: Proc. of the 11th Annual Conf.on Computational Learning Theory. New York: ACM Press,1998. 80--91.
6T. Joachims. Text categorization with support vector machines:Learning with many relevant features. In: Proc. of the 10th European Conf. on Machine Learning. New York: Springer,1998. 137-142.
7Y. Yang. An evaluation of statistical approaches to text categorization. Information Retrieval, 1999, 1 ( 1 ) : 76-- 88.
8R. Adwait. Maximum entropy models for natural language ambiguity resolution: [ Ph. D. dissertation ] . Pennsylvania:University of Pennsylvania, 1998.
9R. Adwait. A maximum entropy model for part-of-speech tagging. The Empirical Methods in Natural Language Processing Conference, Philadelphia, USA, 1996.
10Adam L. Berger, Stephen A. Della Pietra, Vincent J. Della Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 1996, 22( 1 ) : 38-- 73.