dc.contributor | Univ Western Sao Paulo UNOESTE | |
dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2019-10-04T20:36:23Z | |
dc.date.accessioned | 2022-12-19T18:18:10Z | |
dc.date.available | 2019-10-04T20:36:23Z | |
dc.date.available | 2022-12-19T18:18:10Z | |
dc.date.created | 2019-10-04T20:36:23Z | |
dc.date.issued | 2014-01-01 | |
dc.identifier | 2014 Ieee Symposium On Swarm Intelligence (sis). New York: Ieee, p. 8-13, 2014. | |
dc.identifier | http://hdl.handle.net/11449/186400 | |
dc.identifier | WOS:000364912700003 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5367436 | |
dc.description.abstract | The 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.language | eng | |
dc.publisher | Ieee | |
dc.relation | 2014 Ieee Symposium On Swarm Intelligence (sis) | |
dc.rights | Acesso aberto | |
dc.source | Web of Science | |
dc.subject | Support Vector Machines | |
dc.subject | Social-Spider Optimization | |
dc.subject | Evolutionary Computing | |
dc.title | A Social-Spider Optimization Approach for Support Vector Machines Parameters Tuning | |
dc.type | Actas de congresos | |