Adapting two-class support vector classification methods to many class problems

Conference item

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.

Authors: Hill, S; Doucet, A

• Host title: ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
• Pages: 313-320
• Publication date: 2005-01-01
• ISBN: 1595931805