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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 they behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for... (Update)
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BibTeX entry: (Update)
Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. In Machine Learning: ECML-98, Tenth European Conference on Machine Learning, pp. 137--142. http://citeseer.ist.psu.edu/joachims97text.html More
@inproceedings{ joachims98text,
author = "Thorsten Joachims",
title = "Text categorization with support vector machines: learning with many relevant features",
booktitle = "Proceedings of {ECML}-98, 10th European Conference on Machine Learning",
number = "1398",
publisher = "Springer Verlag, Heidelberg, DE",
address = "Chemnitz, DE",
editor = "Claire N{\'{e}}dellec and C{\'{e}}line Rouveirol",
pages = "137--142",
year = "1998",
url = "citeseer.ist.psu.edu/joachims97text.html" }
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255 A training algorithm for optimal margin classifiers - Boser, Guyon et al. - 1992
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