|| F. Sebastiani, “Machine Learning in Automated Text Categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.|
 R.E. Bellman, Dynamic Programming. Princeton University Press, Princeton, NJ, 1957.
 L. D. Baker and A. McCallum, “Distributional Clustering of Words for Text Classification,” 21st Annual International ACM SIGIR, pp. 96-103, 1998
 N. Slonim and N. Tishby, “The Power of Word Clusters for Text Classification,” 23rd European Colloquium on Information Retrieval Research (ECIR), 2001.
 M. Steinbach, G. Karypis, and V. Kumar, “A Comparison of Document Clustering Techniques,” KDD Workshop on Text Mining, Technical report of University of Minnesota, 2000.
 T. Joachims, “Text Categorization with Support Vector Machines: Learning with Many Relevant Features,” Proceedings of the European Conference on Machine Learning (ECML), Springer, 1998.
 D. D. Lewis, R. E. Schapire, J. P. Callan, and R. Papka, "Training Algorithms for Linear Text Classifiers," ACM SIGIR'96, Zurich, Switzerland, August 1996, pp. 298-306.
 Eui-Hong Han, George Karypis, and Vipin Kumar, Text Categorization Using Weight Adjusted k -Nearest Neighbor Classification. Springer Berlin, 2001.
 W. Lam, C.Y. Ho, “Using A Generalized Instance Set for Automatic Text Categorization,” Proceedings of the 21th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98), pp. 81-89, 1998.
 C. Aptê, F. Damerau, and S. Weiss, "Automated Learning of
Decision Rules for Text Categorization," ACM Transactions on
Information Systems, 12(2):233-251, 1994.
 R. E. Schapire and Y. Singer, “BoosTexter: A Boosting-based System for Text Categorization,” Machine Learning (39:2-3) 2000, pp. 135-168.
 L. M. Manevitz and M. Yousef, “Document Classification on Neural Networks Using Only Positive Examples,” Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 3604-306, 2000.
 Y. Yang and X. Liu, “A Re-examination of Text Categorization Methods,” Proceedings 37 of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42-49, 1999.
 D. D. Lewis, R. E. Schapire, J. P. Callan, and R. Papka, “Training algorithms for linear text classifiers,” Proceedings of the 19th International Annual ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 298-306, 1996.
 E. H. Han and G. Karypis, “Centroid-Based Document Classification: Analysis &Experimental Results,” Technical Report #00-017, 2000.
 W. Lam and C. Y. Ho, “Using A Generalized Instance Set for Automatic Text Categorization,” Proceedings of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 81-89, 1998.
 Y. Yang and J. O. Pedersen, “A Comparative Study on Feature Selection in Text Categorization,” 14th International Conference on Machine Learning, pp. 412-420, 1997
 I. T. Jolliffe, Principal Component Analysis. Springer-Verlag, 1986.
 A. M. Martinez and A. C. Kak, “PCA versus LDA,,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001.
 H. Li, T. Jiang, and K. Zang, “Efficient and Robust Feature Extraction by Maximum Margin Criterion,” Conference on Advances in Neural Information Processing System, pp. 97-104, 2004.
 J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, pp. 2319-2323, 2000.
 S. T. Roweis and L. K. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, pp. 2323-2326, 2000.
 M. Belkin and P. Niyogi, “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,” Advances in Neural Information Processing Systems 14, 2002.
 K. Hiraoka, K. Hidai, M. Hamahira, H. Mizoguchi, T. Mishima, and S. Yoshizawa, “Successive Learning of Linear Discriminant Analysis: Sanger-Type Algorithm,” 14th International Conference on Pattern Recognition, pp. 2664-2667, 2000.
 J. Weng, Y. Zhang, and W. S. Hwang, “Candid Covariance-Free Incremental Principal Component Analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1034-1040, 2003.
 J. Yan, B. Y. Zhang, S. C. Yan, Z. Chen, W. G. Fan, Q. Yang, W. Y. Ma, and Q. S. Cheng, “IMMC: Incremental Maximum Margin Criterion,” 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 725-730, 2004
 J.Yan, B. Zhang, N. Liu, S. Yan, Q. Cheng, W. Fan, Q. Yang, W. Xi, and Z. Chen, “Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 3, pp. 320-331, 2006.
 Y. Yang and J. Pedersen, “A Comparative Study on Feature Selection in Text Categorization,” Proceedings of the International Conference on Machine Learning (ICML’97), pp. 412-420, 1997.
 F. Pereira, N. Tishby, and L. Lee, “Distributional Clustering of English Words,” 31st Annual Meeting of ACL, pp. 183-190, 1993.
 I. S. Dhillon, S. Mallela, and R. Kumar, “A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification,” Journal of Machine Learning Research, vol. 3, pp. 1265-1287, 2003.
 B.-Y. Ricardo and R.-N. Berthier, Modern Information Retrieval. Addison Wesley Longman, 1999.
 Z. Harris, "Distributional Structure," Word 10 (2/3): 146-62, 1954
 Joel Larocca Neto, Alexandre D. Santos, Celso A.A. Kaestner, and Alex A. Freitas, “Document Clustering and Text Summarization,” Proceedings 4th International Conference Practical Applications of Knowledge Discovery and Data Mining (PADD-2000), pp. 41-55, London, 2000.
 W. B. Frakes and R. Baeza-Yates, Information Retrieval: Data Structure and Algorithms. Prentice Hall, Englwood Cliffs, NJ, USA, 1992.
 R. Bekkerman, R. El-Yaniv, N. Tishby, and Y. Winter, “Distributional Word Clusters vs. Words for Text Categorization,” Journal of Machine Learning Research, vol. 3, pp. 1183-1208, 2003.
 S.-J. Lee and C.-S. Ouyang, "A Neuro-Fuzzy System Modeling with Self-Constructing Rule Generation and Hybrid SVD-Based Learning, " IEEE Transactions on Fuzzy Systems, Vol. 11, No. 3, pp. 341-353, Jun. 2003.
 J. Yen and R. Langari, Fuzzy Logic - Intelligence, Control, and Information. Prentice-Hall, Upper Saddle River, NJ, USA, 1999.
 J. S. Wang and C. S. G. Lee, “Self-Adaptive Neurofuzzy Inference Systems for Classification Applications,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 6, pp. 790-802, 2002
 C. C. Chang and C. J. Lin, “Libsvm: A Library for Support Vector Machines,” 2001, software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm
 The Cade Web directory, http//www.cade.com.br/
 B. Larsen and C. Aone, “Fast and Effective Text Mining Using Linear-time Document Clustering,” KDD-99, California, 1999.