Text categorization with support vector machines:
learning with many relevant features
T. Joachims. Proceedings of ECML-98, 10th European Conference on
Machine Learning, page 137--142. Chemnitz, DE, Springer Verlag, Heidelberg, DE, (1998)Published in the ``Lecture Notes in Computer Science''
series, number 1398.
Abstract
The paper explores the use of Support Vector Machines
(SVMs) for learning text classifiers from examples. It
analyzes the particular properties of learning with
text data and identifies why SVMs are appropriate for
this task. Empirical results support the theoretical
findings. SVMs achieve substantial improvements over
the currently best performing methods and behave
robustly over a variety of different learning tasks.
Furthermore, they are fully automatic, eliminating the
need for manual parameter tuning.
%0 Conference Paper
%1 Joachims98
%A Joachims, Thorsten
%B Proceedings of ECML-98, 10th European Conference on
Machine Learning
%C Chemnitz, DE
%D 1998
%E Nédellec, Claire
%E Rouveirol, Céline
%I Springer Verlag, Heidelberg, DE
%K imported
%P 137--142
%T Text categorization with support vector machines:
learning with many relevant features
%U http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_98a.ps.gz
%X The paper explores the use of Support Vector Machines
(SVMs) for learning text classifiers from examples. It
analyzes the particular properties of learning with
text data and identifies why SVMs are appropriate for
this task. Empirical results support the theoretical
findings. SVMs achieve substantial improvements over
the currently best performing methods and behave
robustly over a variety of different learning tasks.
Furthermore, they are fully automatic, eliminating the
need for manual parameter tuning.
@inproceedings{Joachims98,
abstract = {The paper explores the use of Support Vector Machines
(SVMs) for learning text classifiers from examples. It
analyzes the particular properties of learning with
text data and identifies why SVMs are appropriate for
this task. Empirical results support the theoretical
findings. SVMs achieve substantial improvements over
the currently best performing methods and behave
robustly over a variety of different learning tasks.
Furthermore, they are fully automatic, eliminating the
need for manual parameter tuning.},
added-at = {2009-01-22T05:56:16.000+0100},
address = {Chemnitz, DE},
author = {Joachims, Thorsten},
biburl = {https://www.bibsonomy.org/bibtex/2619a33e5e90fe7cb6c28dcdcf217440b/kabloom},
booktitle = {Proceedings of ECML-98, 10th European Conference on
Machine Learning},
editor = {N{\'{e}}dellec, Claire and Rouveirol, C{\'{e}}line},
interhash = {997f731cfc4fdb02cb32eb88c4fab2e9},
intrahash = {619a33e5e90fe7cb6c28dcdcf217440b},
keywords = {imported},
note = {Published in the ``Lecture Notes in Computer Science''
series, number 1398},
pages = {137--142},
publisher = {Springer Verlag, Heidelberg, DE},
timestamp = {2011-03-09T04:36:58.000+0100},
title = {Text categorization with support vector machines:
learning with many relevant features},
url = {http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_98a.ps.gz},
year = 1998
}