dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2015-03-18T15:56:37Z
dc.date.available2015-03-18T15:56:37Z
dc.date.created2015-03-18T15:56:37Z
dc.date.issued2013-01-01
dc.identifier2013 Ieee Eurocon. New York: Ieee, p. 998-1002, 2013.
dc.identifierhttp://hdl.handle.net/11449/117647
dc.identifierWOS:000343135600145
dc.identifier9039182932747194
dc.description.abstractSince the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that aims to make SVM less prone to errors over the training set. In this paper, we model the problem of finding such parameters as a metaheuristic-based optimization task, which is performed through Harmony Search (HS) and some of its variants. The experimental results have showen the robustness of HS-based approaches for such task in comparison against with an exhaustive (grid) search, and also a Particle Swarm Optimization-based implementation.
dc.languageeng
dc.publisherIeee
dc.relation2013 Ieee Eurocon
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectSupport Vector Machines
dc.subjectHarmony Search
dc.subjectFault Detections
dc.titleHarmony Search applied for Support Vector Machines Training Optimization
dc.typeActas de congresos


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