A framework for Kernel-based multi-category classification

Journal article

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. © 2007 AI Access Foundation. All rights reserved.

Authors: Hill, S; Doucet, A

• Publication status: Published
• Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
• Volume: 30
• Pages: 525-564
• Publication date: 2007-01-01
• EISSN: 1076-9757
• ISSN: 1076-9757
• Language: English