Pattern classification with mixtures of weighted least-squares support vector machine experts

dc.contributor.authorLima C.A.M.
dc.contributor.authorCoelho A.L.V.
dc.contributor.authorVonZuben F.J.
dc.date.accessioned2024-03-13T01:34:23Z
dc.date.available2024-03-13T01:34:23Z
dc.date.issued2009
dc.description.abstractSupport Vector Machine (SVM) classifiers are high-performance classification models devised to comply with the structural risk minimization principle and to properly exploit the several SVM variants, Least-Squares SVMs (LS-SVMs) have gained increased attention recently due mainly to their computationally attractive properties coming as the direct result of applying a modified formulation that makes use of a sum-squared-error cost function jointly with equality, instead of inequality, constraints. In this work, we present a flexible hybrid approach aimed at augmenting the proficiency of LS-SVM classifiers with regard to accuracy/generalization as well as to hyperparameter calibration issues. Such approach, named as Mixtures of Weighted Least-Squares Support Vector Machine Experts, centers around the fusion of the weighted variant of LS-SVMs with Mixtures of Experts models. After the formal characterization of the novel learning framework, simulation results obtained with respect to both binary and multiclass pattern classification problems are reported, ratifying the suitability of the novel hybrid approach in improving the performance issues considered. © Springer-Verlag London Limited 2008.
dc.description.firstpage843
dc.description.issuenumber7
dc.description.lastpage860
dc.description.volume18
dc.identifier.doi10.1007/s00521-008-0210-6
dc.identifier.issn0941-0643
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37343
dc.relation.ispartofNeural Computing and Applications
dc.rightsAcesso Restrito
dc.subject.otherlanguageHybridization
dc.subject.otherlanguageLeast squares support vector machines
dc.subject.otherlanguageMixtures of experts
dc.subject.otherlanguagePattern classification
dc.titlePattern classification with mixtures of weighted least-squares support vector machine experts
dc.typeArtigo
local.scopus.citations5
local.scopus.eid2-s2.0-70350145678
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=70350145678&origin=inward
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