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

Abstract

This paper explores the use of Support Vector Machines (SVMs) for learning text classiiers from examples. It analyzes the particular properties of learning with text data and identiies why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of diierent learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning.

DOI: 10.1007/BFb0026683

Extracted Key Phrases

Showing 1-10 of 10 references

An evaluation of statistical approaches to text categorization

  • Y Yang
  • 1997

Machine Learning

  • T Mitchell
  • 1997

Relevance feedback in information retrieval The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313{323

  • J Rocchio
  • 1971
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