Comments on: Support Vector Machines Maximizing Geometric Margins for Multi-class Classification 2
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Yann Guermeur [1]
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[1] LORIA-CNRS, Francia
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- Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 22, Nº. 3, 2014, págs. 844-851
- Idioma: inglés
- DOI: 10.1007/s11750-014-0340-1
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Resumen
The article deals with multi-class discrimination with support vector machines (SVMs). The authors present multi-class SVMs (MSVMs) which they have introduced in recent years: multiobjective MSVMs (MMSVMs). Those machines are based on the same functional class as that of the standard MSVMs (Guermeur 2012). They differ in the nature of the learning problem, which is no longer a standard optimization problem (convex quadratic programming problem), but a multiobjective optimization problem (taking the form of a second-order cone programming problem). The aim is to maximize exactly all geometric margins, so as to improve generalization performance. This performance is assessed empirically, through experiments performed on data sets from the UCI benchmark repository. In our comments, we make use of the latest results of the statistical theory of large margin multi-category classifiers to study the connection between the (width of the) geometric margins and the generalization performance.
