Text categorization with support vector machines: learning with many relevant features
T. Joachims. Proceedings of ECML-98, 10th European Conference on Machine Learning, 1398, page 137--142. Chemnitz, DE, Springer Verlag, Heidelberg, DE, (1998)
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...
%0 Conference Paper
%1 citeulike:437141
%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 information_retrieval kernel_methods svm textmining
%N 1398
%P 137--142
%T Text categorization with support vector machines: learning with many relevant features
%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...
@inproceedings{citeulike:437141,
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...},
added-at = {2008-03-05T15:24:21.000+0100},
address = {Chemnitz, DE},
author = {Joachims, Thorsten},
biburl = {https://www.bibsonomy.org/bibtex/2d31522a42571f0adb834c5956540716e/danielt},
booktitle = {Proceedings of ECML-98, 10th European Conference on Machine Learning},
editor = {N\'{e}dellec, Claire and Rouveirol, C\'{e}line},
interhash = {997f731cfc4fdb02cb32eb88c4fab2e9},
intrahash = {d31522a42571f0adb834c5956540716e},
keywords = {information_retrieval kernel_methods svm textmining},
number = 1398,
pages = {137--142},
publisher = {Springer Verlag, Heidelberg, DE},
timestamp = {2008-03-05T15:24:21.000+0100},
title = {Text categorization with support vector machines: learning with many relevant features},
year = 1998
}