Text categorization with Support Vector Machines: Learning with many relevant features
T. Joachims. chapter Text categorization with Support Vector Machines: Learning with many relevant features, page 137--142. (1998)
DOI: 10.1007/BFb0026683
Abstract
This 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 Book Section
%1 Joachims1998
%A Joachims, Thorsten
%D 1998
%K SVM classification inex08paper text
%P 137--142
%R 10.1007/BFb0026683
%T Text categorization with Support Vector Machines: Learning with many relevant features
%U http://dx.doi.org/10.1007/BFb0026683
%X This 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.
%& Text categorization with Support Vector Machines: Learning with many relevant features
@inbook{Joachims1998,
abstract = {This 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 = {2008-12-02T04:17:46.000+0100},
author = {Joachims, Thorsten},
biburl = {https://www.bibsonomy.org/bibtex/2cf9d5daf68dbdde20402cb317fb9f1a7/cdevries},
chapter = {Text categorization with Support Vector Machines: Learning with many relevant features},
doi = {10.1007/BFb0026683},
interhash = {997f731cfc4fdb02cb32eb88c4fab2e9},
intrahash = {cf9d5daf68dbdde20402cb317fb9f1a7},
keywords = {SVM classification inex08paper text},
pages = {137--142},
timestamp = {2009-03-23T09:10:10.000+0100},
title = {Text categorization with Support Vector Machines: Learning with many relevant features},
url = {http://dx.doi.org/10.1007/BFb0026683},
year = 1998
}