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

Proceedings of ECML-98, 10th European Conference on Machine Learning, (1398): 137--142, 1998.
Authors: Thorsten Joachims
Editors: Claire N\'{e}dellec and C\'{e}line Rouveirol
Tags: information_retrieval kernel_methods svm textmining
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...
| BibTeX  
@inproceedings{citeulike:437141,
title = {Text categorization with support vector machines: learning with many relevant features},
address = {Chemnitz, DE},
author = {Thorsten Joachims},
booktitle = {Proceedings of ECML-98, 10th European Conference on Machine Learning},
editor = {Claire N\'{e}dellec and C\'{e}line Rouveirol},
number = {1398},
pages = {137--142},
publisher = {Springer Verlag, Heidelberg, DE},
year = {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...},
keywords = {information_retrieval kernel_methods svm textmining }
}