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dc.contributor.authorPereira, Luís Augusto Martins-
dc.contributor.authorRodrigues, Douglas-
dc.contributor.authorRibeiro, Patricia Bellin-
dc.contributor.authorPapa, João Paulo-
dc.contributor.authorWeber, Silke Anna Theresa-
dc.identifier.citation2014 Ieee 27th International Symposium On Computer-based Medical Systems (cbms). New York: Ieee, p. 14-17, 2014.-
dc.description.abstractEvolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.en
dc.sourceWeb of Science-
dc.subjectArtificial Neural Networksen
dc.subjectParkinsons' Diseaseen
dc.subjectSocial-Spider Optimizationen
dc.titleSocial-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identificationen
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUNESP Univ Estadual Paulista, Dept Comp, Sao Paulo, Brazil-
dc.description.affiliationUnespUNESP Univ Estadual Paulista, Dept Comp, Sao Paulo, Brazil-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartof2014 Ieee 27th International Symposium On Computer-based Medical Systems (cbms)-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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