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...

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Detalles Bibliográficos
Autores: Carrizosa Priego, Emilio José, Nogales Gómez, Amaya, Romero Morales, María Dolores
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2014
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/44819
Acceso en línea:http://hdl.handle.net/11441/44819
https://doi.org/10.1007/s11590-013-0630-9
Access Level:acceso abierto
Palabra clave:Support vector machines
Ramp loss
Mixed integer nonlinear programming
Heuristics
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spelling Heuristic approaches for support vector machines with the ramp lossCarrizosa Priego, Emilio JoséNogales Gómez, AmayaRomero Morales, María DoloresSupport vector machinesRamp lossMixed integer nonlinear programmingHeuristicsRecently, 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.Ministerio de Economía y CompetitividadJunta de AndalucíaEuropean Regional Development FundsSpringerEstadística e Investigación OperativaFQM329: Optimizacion2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/44819https://doi.org/10.1007/s11590-013-0630-9reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésOptimization Letters, 8 (3), 1125-1135.info:eu-repo/grantAgreement/MINECO/MTM2012-36163/FQM-329http://download.springer.com/static/pdf/646/art%253A10.1007%252Fs11590-013-0630-9.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11590-013-0630-9&token2=exp=1473328439~acl=%2Fstatic%2Fpdf%2F646%2Fart%25253A10.1007%25252Fs11590-013-0630-9.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Farticle%252F10.1007%252Fs11590-013-0630-9*~hmac=5735776a875bdee2ff73395d96cf8f3d1095949869db89f2ed6c635ffc3fed56info:eu-repo/semantics/openAccessoai:idus.us.es:11441/448192026-06-17T12:51:07Z
dc.title.none.fl_str_mv Heuristic approaches for support vector machines with the ramp loss
title Heuristic approaches for support vector machines with the ramp loss
spellingShingle Heuristic approaches for support vector machines with the ramp loss
Carrizosa Priego, Emilio José
Support vector machines
Ramp loss
Mixed integer nonlinear programming
Heuristics
title_short Heuristic approaches for support vector machines with the ramp loss
title_full Heuristic approaches for support vector machines with the ramp loss
title_fullStr Heuristic approaches for support vector machines with the ramp loss
title_full_unstemmed Heuristic approaches for support vector machines with the ramp loss
title_sort Heuristic approaches for support vector machines with the ramp loss
dc.creator.none.fl_str_mv Carrizosa Priego, Emilio José
Nogales Gómez, Amaya
Romero Morales, María Dolores
author Carrizosa Priego, Emilio José
author_facet Carrizosa Priego, Emilio José
Nogales Gómez, Amaya
Romero Morales, María Dolores
author_role author
author2 Nogales Gómez, Amaya
Romero Morales, María Dolores
author2_role author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM329: Optimizacion
dc.subject.none.fl_str_mv Support vector machines
Ramp loss
Mixed integer nonlinear programming
Heuristics
topic Support vector machines
Ramp loss
Mixed integer nonlinear programming
Heuristics
description 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.
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/44819
https://doi.org/10.1007/s11590-013-0630-9
url http://hdl.handle.net/11441/44819
https://doi.org/10.1007/s11590-013-0630-9
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Optimization Letters, 8 (3), 1125-1135.
info:eu-repo/grantAgreement/MINECO/MTM2012-36163/
FQM-329
http://download.springer.com/static/pdf/646/art%253A10.1007%252Fs11590-013-0630-9.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11590-013-0630-9&token2=exp=1473328439~acl=%2Fstatic%2Fpdf%2F646%2Fart%25253A10.1007%25252Fs11590-013-0630-9.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Farticle%252F10.1007%252Fs11590-013-0630-9*~hmac=5735776a875bdee2ff73395d96cf8f3d1095949869db89f2ed6c635ffc3fed56
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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