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 | | From: | Redistributed | | Subject: | JMLR: Dimension Reduction in Text Classification with SVMs | | Date: | Thu, 20 Jan 2005 18:54:42 GMT |
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 | [[Redistributed from JMLR announce]]
~From: elm@cs.umass.edu
~Date: Sun, 9 Jan 2005 14:05:52 -0500
~Subject: [Jmlr-announce] Dimension Reduction in Text Classification with Support Vector Machines
The Journal of Machine Learning Research (www.jmlr.org) is pleased to
announce publication of a new paper:
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Dimension Reduction in Text Classification with Support Vector Machines
Hyunsoo Kim, Peg Howland and Haesun Park
JMLR 6 (Jan): 37--53, 2005
Abstract
Support vector machines (SVMs) have been recognized as one of the most
successful classification methods for many applications including text
classification. Even though the learning ability and computational
complexity of training in support vector machines may be independent of
the dimension of the feature space, reducing computational complexity
is an essential issue to efficiently handle a large number of terms in
practical applications of text classification. In this paper, we adopt
novel dimension reduction methods to reduce the dimension of the
document vectors dramatically. We also introduce decision functions for
the centroid-based classification algorithm and support vector
classifiers to handle the classification problem where a document may
belong to multiple classes. Our substantial experimental results show
that with several dimension reduction methods that are designed
particularly for clustered data, higher efficiency for both training
and testing can be achieved without sacrificing prediction accuracy of
text classification even when the dimension of the input space is
significantly reduced.
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This paper and previous papers are available electronically at
http://www.jmlr.org in PDF format. The papers of Volumes 1-4 were also
published in hardcopy by MIT Press; please see
http://mitpress.mit.edu/JMLR for details. Volume 5 and subsequent
volumes will be printed in hardcopy by Microtome Publishing. Please see
http://www.mtome.com/Publications/jmlr.html for details and ordering
information.
-Erik G. Learned-Miller
elm@cs.umass.edu
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http://lists.csail.mit.edu/mailman/listinfo/jmlr-announce
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