Text Categorization with Support Vector Machines: Learning with Many Relevant Features (1997)
| Citations: | 1 - 0 self |
BibTeX
@MISC{Informatik97textcategorization,
author = {Fachbereich Informatik and Lehrstuhl Viii and Kunstliche Intelligenz and Fachbereich Informatik and Thorsten Joachims and Anforderungen An and Fachbereich Informatik and Lehrstuhl Viii and Fachbereich Informatik and Lehrstuhl Viii and Fachbereich Informatik and Thorsten Joachims},
title = {Text Categorization with Support Vector Machines: Learning with Many Relevant Features},
year = {1997}
}
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Abstract
This paper explores the use of Support Vector Machines (SVMs) for learning text classiers from examples. It analyzes the particular properties of learning with text data and identi es, why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly overavariety of di erent learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning. 1
Citations
| 739 | JP: A Comparative Study on Feature Selection in Text Categorization - Yang, Pedersen - 1997 |
| 17 | Using corpus statistics to remove redundant words in text categorization - Yang, Wilbur - 1996 |
