A Framework for Kernel-Based Multi-Category Classification

A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vecto...

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Bibliographic Details
Published inThe Journal of artificial intelligence research Vol. 30; pp. 525 - 564
Main Authors Hill, S. I., Doucet, A.
Format Journal Article
LanguageEnglish
Published San Francisco AI Access Foundation 01.01.2007
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Summary:A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vector Classification, with parallels drawn to related methods. The ability of the framework to compare algorithms is illustrated by a brief discussion of Fisher consistency. Its utility in improving understanding of multi-category analysis is demonstrated through a derivation of improved generalisation bounds. It is also described how this architecture provides insights regarding how to further improve on the speed of existing multi-category classification algorithms. An initial example of how this might be achieved is developed in the formulation of a straightforward multi-category Sequential Minimal Optimisation algorithm. Proof-of-concept experimental results have shown that this, combined with the mapping of pairwise results, is comparable with benchmark optimisation speeds.
ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.2251