A Framework for Kernel-Based Multi-Category Classification

• Published in: The Journal of artificial intelligence research Vol. 30; pp. 525 - 564
• Main Authors: Hill, S. I., Doucet, A.
• Format: Journal Article
• Language: English
• Published: San Francisco AI Access Foundation 01.01.2007
• Subjects: Algorithms; Artificial intelligence; Classification; Optimization
• ISSN: 1076-9757; 1076-9757; 1943-5037
• DOI: 10.1613/jair.2251

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.