dc.contributorUniv Western Sao Paulo UNOESTE
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-04T20:36:23Z
dc.date.accessioned2022-12-19T18:18:10Z
dc.date.available2019-10-04T20:36:23Z
dc.date.available2022-12-19T18:18:10Z
dc.date.created2019-10-04T20:36:23Z
dc.date.issued2014-01-01
dc.identifier2014 Ieee Symposium On Swarm Intelligence (sis). New York: Ieee, p. 8-13, 2014.
dc.identifierhttp://hdl.handle.net/11449/186400
dc.identifierWOS:000364912700003
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5367436
dc.description.abstractThe choice of hyper-parameters in Support Vector Machines (SVM)-based learning is a crucial task, since different values may degrade its performance, as well as can increase the computational burden. In this paper, we introduce a recently developed nature-inspired optimization algorithm to find out suitable values for SVM kernel mapping named Social-Spider Optimization (SSO). We compare the results obtained by SSO against with a Grid-Search, Particle Swarm Optimization and Harmonic Search. Statistical evaluation has showed SSO can outperform the compared techniques for some sort of kernels and datasets.
dc.languageeng
dc.publisherIeee
dc.relation2014 Ieee Symposium On Swarm Intelligence (sis)
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectSupport Vector Machines
dc.subjectSocial-Spider Optimization
dc.subjectEvolutionary Computing
dc.titleA Social-Spider Optimization Approach for Support Vector Machines Parameters Tuning
dc.typeActas de congresos


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