Text categorization with Support Vector Machines: Learning with many relevant features

  • Thorsten Joachims
Support Vector Learning

DOI: 10.1007/BFb0026683

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)
Cite this paper as:
Joachims T. (1998) Text categorization with Support Vector Machines: Learning with many relevant features. In: Nédellec C., Rouveirol C. (eds) Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 1398. Springer, Berlin, Heidelberg

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 parameter tuning.

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Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Thorsten Joachims
    • 1
  1. 1.Universität DortmundInforinatik LS8DortmundGermany

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