ML Anthology

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

Thorsten Joachims
ECML-PKDD 1998 pp. 137-142
doi:10.1007/BFB0026683 /ecmlpkdd/1998/joachims1998ecml-text/

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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Cite

Text

Joachims. "Text Categorization with Support Vector Machines: Learning with Many Relevant Features." European Conference on Machine Learning, 1998. doi:10.1007/BFB0026683

Markdown

[Joachims. "Text Categorization with Support Vector Machines: Learning with Many Relevant Features." European Conference on Machine Learning, 1998.](https://mlanthology.org/ecmlpkdd/1998/joachims1998ecml-text/) doi:10.1007/BFB0026683

BibTeX

@inproceedings{joachims1998ecml-text,
  title     = {{Text Categorization with Support Vector Machines: Learning with Many Relevant Features}},
  author    = {Joachims, Thorsten},
  booktitle = {European Conference on Machine Learning},
  year      = {1998},
  pages     = {137-142},
  doi       = {10.1007/BFB0026683},
  url       = {https://mlanthology.org/ecmlpkdd/1998/joachims1998ecml-text/}
}