Heuristic Approaches for Support Vector Machines with the Ramp Loss

Emilio Carrizosa, Amaya Nogales-Gómez, Dolores Romero Morales

Research output: Contribution to journalJournal articleResearchpeer-review


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
Original languageEnglish
JournalOptimization Letters
Issue number3
Pages (from-to)1125-1135
Publication statusPublished - 2014
Externally publishedYes

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