A statistical learning learning model of text classification for support vector machines

  • Authors:
  • Thorsten Joachims

  • Affiliations:
  • GMD Forschungszentrum IT, Sankt Augustin, Germany

  • Venue:
  • Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2001

Quantified Score

Hi-index 0.01

Visualization

Abstract

This paper develops a theoretical learning model of text classification for Support Vector Machines (SVMs). It connects the statistical properties of text-classification tasks with the generalization performance of a SVM in a quantitative way. Unlike conventional approaches to learning text classifiers, which rely primarily on empirical evidence, this model explains why and when SVMs perform well for text classification. In particular, it addresses the following questions: Why can support vector machines handle the large feature spaces in text classification effectively? How is this related to the statistical properties of text? What are sufficient conditions for applying SVMs to text-classification problems successfully?