Artículo
Heuristic approaches for support vector machines with the ramp loss
Autor/es | Carrizosa Priego, Emilio José
Nogales Gómez, Amaya Romero Morales, María Dolores |
Departamento | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Fecha de publicación | 2014-03 |
Fecha de depósito | 2016-09-08 |
Publicado en |
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Resumen | 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 ... 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. |
Identificador del proyecto | info:eu-repo/grantAgreement/MINECO/MTM2012-36163
FQM-329 |
Cita | Carrizosa Priego, E.J., Nogales Gómez, A. y Romero Morales, M.D. (2014). Heuristic approaches for support vector machines with the ramp loss. Optimization Letters, 8 (3), 1125-1135. |
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