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}
}
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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Printed Edition
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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Copyright © Sat Nov 14 05:29:39 2009
by Michael Ley (ley@uni-trier.de)
