Abstract
The All-Distances SVM is a single-objective light extension of the binary μ-SVM for multi-category classification that is competitive against multi-objective SVMs, such as One-against-the-Rest SVMs and One-against-One SVMs. Although the model takes into account considerably less constraints than previous formulations, it lacks of an efficient training algorithm, making its use with medium and large problems impracticable. In this paper, a Sequential Minimal Optimization-like algorithm is proposed to train the All-Distances SVM, making large problems abordable. Experimental results with public benchmark data are presented to show the performance of the AD-SVM trained with this algorithm against other single-objective multi-category SVMs.
This work was supported in part by Research Grant FB0821 “Centro Científico Tecnológico de Valparaíso” UTFSM and by DGIP-UTFSM Grant.
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Candel, D., Ñanculef, R., Concha, C., Allende, H. (2010). A Sequential Minimal Optimization Algorithm for the All-Distances Support Vector Machine. In: Bloch, I., Cesar, R.M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2010. Lecture Notes in Computer Science, vol 6419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16687-7_64
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