Training Algorithms for Linear Text Classifiers.

David D. Lewis, Robert E. Schapire, James P. Callan, Ron Papka: Training Algorithms for Linear Text Classifiers. SIGIR 1996: 298-306

@inproceedings{DBLP:conf/sigir/LewisSCP96,
  author    = {David D. Lewis and
               Robert E. Schapire and
               James P. Callan and
               Ron Papka},
  title     = {Training Algorithms for Linear Text Classifiers},
  booktitle = {SIGIR},
  year      = {1996},
  pages     = {298-306},
  ee        = {db/conf/sigir/LewisSCP96.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

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Abstract

Systems for text retrieval, routing, categorization and other IR tasks rely heavily on linear classifiers. We propose that two machine learning algorithms, the Widrow-Hoff and EG algorithms, be used in training linear text classifiers. In contrast to most IR methods, theoretical analysis provides performance guarantees and guidance on parameter settings for these algorithms. Experimental data is presented showing Widrow-Hoff and EG to be more effective than the widely used Rocchio algorithm on several categorization and routing tasks.

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Hans-Peter Frei, Donna Harman, Peter Schäuble, Ross Wilkinson (Eds.): Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum). ACM 1996, ISBN 0-89791-792-8
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