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dc.contributor.authorQuartier, Benoit
dc.contributor.authorBelanche Muñoz, Luis Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-12-07T11:32:05Z
dc.date.available2016-12-07T11:32:05Z
dc.date.issued2001-10
dc.identifier.citationQuartier, B., Belanche, Ll. "Algorithmes d'entraînement local de RBF". 2001.
dc.identifier.urihttp://hdl.handle.net/2117/97843
dc.description.abstractThe aim of this work is to study the effect of locality in classification tasks with radial basis function neural networks (RBFNN). The networks are trained in a three stage process. Firstly, the data are decomposed in their natural clusters, using clustering algorithms of different complexity. Secondly, a local RBFNN is fit to each cluster. These RBFNNs are local in the sense that they are modeling only a part of the problem, as given by the previous stage. Any RBFNN training algorithm can be used here. Thirdly, the local networks are fused together. We propose several simple techniques to do so. The results are analyzed in light of the following aspects: overall feasibility of the idea, influence of clustering algorithm complexity, influence of specific training algorithms, and selection of the fusing method.
dc.format.extent55 p.
dc.language.isofra
dc.relation.ispartofseriesLSI-01-42-R
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.otherRadial basis function neural networks
dc.subject.otherRBFNN
dc.titleAlgorithmes d'entraînement local de RBF
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.rights.accessOpen Access
local.identifier.drac1902188
dc.description.versionPostprint (published version)
local.citation.authorQuartier, B.; Belanche, Ll.


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