Heuristic Approaches for Support Vector Machines with the Ramp Loss

Emilio Carrizosa, Amaya Nogales-Gómez, Dolores Romero Morales

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstrakt

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
OriginalsprogEngelsk
TidsskriftOptimization Letters
Vol/bind8
Udgave nummer3
Sider (fra-til)1125-1135
ISSN1862-4472
DOI
StatusUdgivet - 2014
Udgivet eksterntJa

Emneord

  • Support vector machines
  • Ramp loss
  • Mixed integer nonlinear programming
  • Heuristics

Citationsformater