Hill, S. I.; Doucet, A. A framework for kernel-based multi-category classification. (English) Zbl 1182.68197 J. Artif. Intell. Res. (JAIR) 30, 525-564 (2007). 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. Cited in 5 Documents MSC: 68T10 Pattern recognition, speech recognition 68W05 Nonnumerical algorithms 68T05 Learning and adaptive systems in artificial intelligence Keywords:Support Vector Classification; Fisher consistency PDFBibTeX XMLCite \textit{S. I. Hill} and \textit{A. Doucet}, J. Artif. Intell. Res. (JAIR) 30, 525--564 (2007; Zbl 1182.68197)