C. Apte, F. Damerau, and S. Weiss, Text mining with decision rules and decision trees, Citeseer, 1998.

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

K. Crammer, O. Dekel, J. Keshet, S. Shalev-shwartz, and Y. Singer, Online passiveaggressive algorithms, Journal of Machine Learning Research, vol.7, pp.551-585, 2006.

F. Debole and F. Sebastiani, Supervised term weighting for automated text categorization, Text mining and its applications, pp.81-97, 2004.
DOI : 10.1145/952686.952688

URL : http://faure.iei.pi.cnr.it/~fabrizio/Publications/SAC03b.pdf

Z. H. Deng, S. W. Tang, D. Q. Yang, M. Z. Li, and K. Q. Xie, A Comparative Study on Feature Weight in Text Categorization, Asia-Pacific Web Conference, pp.588-597, 2004.
DOI : 10.1007/978-3-540-24655-8_64

Z. H. Deng, S. W. Tang, D. Q. Yang, M. Z. Li, and K. Q. Xie, A Comparative Study on Feature Weight in Text Categorization, Advanced Web Technologies and Applications, pp.588-597, 2004.
DOI : 10.1007/978-3-540-24655-8_64

T. Joachims, Text categorization with Support Vector Machines: Learning with many relevant features, European conference on machine learning, pp.137-142, 1998.
DOI : 10.1007/BFb0026683

URL : http://ranger.uta.edu/~alp/ix/readings/SVMsforTextCategorization.pdf

M. Lan, C. L. Tan, J. Su, and Y. Lu, Supervised and traditional term weighting methods for automatic text categorization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.31, issue.4, pp.721-735, 2009.

A. Mccallum and K. Nigam, A comparison of event models for naive bayes text classification, AAAI-98 workshop on learning for text categorization, pp.41-48, 1998.

H. T. Ng, W. B. Goh, and K. L. Low, Feature selection, perception learning, and a usability case study for text categorization, ACM SIGIR Forum, vol.31, issue.SI, pp.67-73, 1997.
DOI : 10.1145/278459.258537

J. R. Quinlan, C4.5: Programs for Machine Learning, 1993.

R. E. Schapire and Y. Singer, Boostexter: A boosting-based system for text categorization, Machine Learning, vol.39, issue.2/3, pp.135-168, 2000.
DOI : 10.1023/A:1007649029923

S. Jones and K. , A STATISTICAL INTERPRETATION OF TERM SPECIFICITY AND ITS APPLICATION IN RETRIEVAL, Journal of Documentation, vol.28, issue.1, pp.11-21, 1972.
DOI : 10.1002/asi.5090180209

R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proceedings of the National Academy of Sciences, vol.26, issue.3, pp.6567-6572, 2002.
DOI : 10.1093/biomet/81.3.425

D. Wang and H. Zhang, Inverse category frequency based supervised term weighting scheme for text categorization. preprint arXiv:1012, pp.2609-2613, 2013.

X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang et al., Top 10 algorithms in data mining, Knowledge and Information Systems, vol.9, issue.2, pp.1-37, 2008.
DOI : 10.1017/CBO9780511815478

URL : http://www.cse.ust.hk/~qyang/Docs/2007/top10.pdf

Y. Yang, Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorization and Retrieval, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp.13-22, 1994.
DOI : 10.1007/978-1-4471-2099-5_2

K. Youngjoong, A study of term weighting schemes using class information for text classification, Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp.1029-1030, 2012.

T. Zhang, Solving large scale linear prediction problems using stochastic gradient descent algorithms, Twenty-first international conference on Machine learning , ICML '04, pp.919-926, 2004.
DOI : 10.1145/1015330.1015332

URL : http://www.aicml.cs.ualberta.ca/banff04/icml/pages/papers/12.ps