Heuristic approaches for support vector machines with the ramp loss
Recently, Support Vector Machines with the ramp loss (RLM) have attracted attention from the computational point of view. In this technical note, we propose two heuristics, the first one based on solving the continuous relaxation of a Mixed Integer Nonlinear formulation of the RLM and the second one...
| Authors: | ; ; |
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| Format: | Article |
| Status: | Submitted version |
| Publication date: | 2014 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/44819 |
| Online Access: | http://hdl.handle.net/11441/44819 https://doi.org/10.1007/s11590-013-0630-9 |
| Access Level: | Open access |
| Keyword: | Support vector machines Ramp loss Mixed integer nonlinear programming Heuristics |
| Summary: | Recently, Support Vector Machines with the ramp loss (RLM) have attracted attention from the computational point of view. In this technical note, we propose two heuristics, the first one based on solving the continuous relaxation of a Mixed Integer Nonlinear formulation of the RLM and the second one based on the training of an SVM classifier on a reduced dataset identified by an integer linear problem. Our computational results illustrate the ability of our heuristics to handle datasets of much larger size than those previously addressed in the literature. |
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