Abstract
Support vector machines are originally designed for binary classification. How to effectively extend it for multi-class classification is still an on-going research issue. In this paper, we consider kernel machines which are natural extensions of multi-category support vector machines originally proposed by Crammer and Singer. Based on the algorithm stability, we obtain the generalization error bounds for the kernel machines proposed in the paper.
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Supported in part by the Specialized Research Fund for the Doctoral Program of Higher Education under grant 20060512001.
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Chen, H., Li, L. Generalization performance of multi-category kernel machines. Analys in Theo Applic 23, 188–195 (2007). https://doi.org/10.1007/s10496-007-0188-4
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DOI: https://doi.org/10.1007/s10496-007-0188-4