Adapting Two-Class Support Vector Classification Methods to Many Class Problems

Hill, Simon I.; Doucet, Arnaud

doi:10.1145/1102351.1102391

Adapting Two-Class Support Vector Classification Methods to Many Class Problems

Simon I. Hill, Arnaud Doucet

ICML 2005 pp. 313-320

doi:10.1145/1102351.1102391 /icml/2005/hill2005icml-adapting/

Abstract

A geometric construction is presented which is shown to be an effective tool for understanding and implementing multi-category support vector classification. It is demonstrated how this construction can be used to extend many other existing two-class kernel-based classification methodologies in a straightforward way while still preserving attractive properties of individual algorithms. Reducing training times through incorporating the results of pairwise classification is also discussed and experimental results presented.

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Cite

Text

Hill and Doucet. "Adapting Two-Class Support Vector Classification Methods to Many Class Problems." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102391

Markdown

[Hill and Doucet. "Adapting Two-Class Support Vector Classification Methods to Many Class Problems." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/hill2005icml-adapting/) doi:10.1145/1102351.1102391

BibTeX

@inproceedings{hill2005icml-adapting,
  title     = {{Adapting Two-Class Support Vector Classification Methods to Many Class Problems}},
  author    = {Hill, Simon I. and Doucet, Arnaud},
  booktitle = {International Conference on Machine Learning},
  year      = {2005},
  pages     = {313-320},
  doi       = {10.1145/1102351.1102391},
  url       = {https://mlanthology.org/icml/2005/hill2005icml-adapting/}
}