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    Colas, F. & Brazdil, P. On the Behavior of SVM and Some Older Algorithms in Binary Text Classification Tasks 2006 Text, Speech and Dialogue, pp. 45-52  article URL 
    Abstract: Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature
    ace transformation whereas some others compared the performance of different algorithms. Recently, following the risinginterest towards the Support Vector Machine, various studies showed that the SVM outperforms other classification algorithms.So should we just not bother about other classification algorithms and opt always for SVM?
      author = {Colas, Fabrice and Brazdil, Pavel},
      title = {On the Behavior of SVM and Some Older Algorithms in Binary Text Classification Tasks},
      journal = {Text, Speech and Dialogue},
      year = {2006},
      pages = {45--52},
      url = {http://dx.doi.org/10.1007/11846406_6}
    Popescul, A., Ungar, L.H., Lawrence, S. & Pennock, D.M. Statistical Relational Learning for Document Mining. 2003 ICDM, pp. 275-282  inproceedings URL 
      author = {Popescul, Alexandrin and Ungar, Lyle H. and Lawrence, Steve and Pennock, David M.},
      title = {Statistical Relational Learning for Document Mining.},
      booktitle = {ICDM},
      publisher = {IEEE Computer Society},
      year = {2003},
      pages = {275-282},
      url = {http://www.cis.upenn.edu/~popescul/Publications/popescul03dm.pdf}
    Sebastiani, F. Machine learning in automated text categorization 2002 ACM Computing Surveys
    Vol. 34(1), pp. 1-47 
    article URL 
      author = {Sebastiani, F.},
      title = {Machine learning in automated text categorization},
      journal = {ACM Computing Surveys},
      year = {2002},
      volume = {34},
      number = {1},
      pages = {1--47},
      url = {http://faure.iei.pi.cnr.it/~fabrizio/Publications/ACMCS02.pdf}

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