Text categorization with support vector machines: learning with many relevant features

by: Thorsten Joachims

edited by: Claire Nédellec, Céline Rouveirol

No. 1398. (1998), pp. 137-142.

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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...

@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...}, address = {Chemnitz, DE}, author = {Joachims, Thorsten }, booktitle = {Proceedings of ECML-98, 10th European Conference on Machine Learning}, citeulike-article-id = {437141}, editor = {N\'{e}dellec, Claire and Rouveirol, C\'{e}line }, keywords = {categorization, classificiation, information-retrieval, informatioretrieval, ir, search}, number = {1398}, pages = {137--142}, posted-at = {2005-12-14 01:37:30}, priority = {2}, publisher = {Springer Verlag, Heidelberg, DE}, title = {Text categorization with support vector machines: learning with many relevant features}, url = {http://citeseer.ist.psu.edu/141654.html}, year = {1998} }

RIS record

TY - CONF ID - citeulike:437141 L3 - citeulike-article-id:437141 TI - Text categorization with support vector machines: learning with many relevant features T2 - Proceedings of ECML-98, 10th European Conference on Machine Learning IS - 1398 SP - 137 EP - 142 AD - Chemnitz, DE PB - Springer Verlag, Heidelberg, DE N2 - 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... KW - categorization KW - classificiation KW - information-retrieval KW - informatioretrieval KW - ir KW - search AU - Joachims, Thorsten A2 - N\'{e}dellec, Claire A2 - Rouveirol, C\'{e}line PY - 1998/// UR - http://citeseer.ist.psu.edu/141654.html ER -

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