Text Categorization with Support Vector Machines: Learning with Many Relevant Features

Text Categorization with Support Vector Machines: Learning with Many Relevant Features (1998)

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by Thorsten Joachims

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@MISC{Joachims98textcategorization,
    author = {Thorsten Joachims},
    title = {Text Categorization with Support Vector Machines: Learning with Many Relevant Features},
    year = {1998}
}

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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 manual parameter tuning.

Citations

884	A Comparative Study on Feature Selection in Text Categorization - Yang, Pedersen
18	Using corpus statistics to remove redundant words in text categorization - Yang, Wilbur - 1996