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

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.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Simon I. Hill, Arnaud Doucet
• Keywords: ICML 2005, Kernel-based Classification Methodologies, Machine Learning, Pairwise Classification, Support Vector Classification