| Título: | Least 1-Norm SVMs: a New SVM Variant between Standard and LS-SVMs. 18th ESANN, Proceedings. ESANN. 135-140. |
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| Autor: | López, J. & Dorronsoro, J.R. |
| Año: | 2010 |
| Enlace: | https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2010-115.pdf |
Abstract
Least Squares Support Vector Machines (LS-SVMs) were proposed by replacing the inequality constraints inherent to L1-SVMs with equality constraints. So far this idea has only been suggested for a least squares (L2) loss. We describe how this can also be done for the sumof-slacks (L1) loss, yielding a new classifier (Least 1-Norm SVMs) which gives similar models in terms of complexity and accuracy and that may also be more robust than LS-SVMs with respect to outliers.