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Creating Modular-like Ensembles by Output Clustering

dc.contributor.authorMora Jiménez, Inma
dc.contributor.authorLyhyaoui, Abdelouahid
dc.contributor.authorArenas García, J.
dc.contributor.authorFigueiras Vidal, Aníbal R
dc.date.accessioned2009-07-30T09:15:03Z
dc.date.available2009-07-30T09:15:03Z
dc.date.issued2009-07-30T09:15:03Z
dc.identifier.urihttp://hdl.handle.net/10115/2605
dc.description.abstractIn this paper we consider the possibility of replacing the output layer of Multi- Layer Perceptrons (MLPs) by local schemes when dealing with classification problems. In order to open the possibility of developing LMS-trainable models, and posterior adaptive schemes, we apply a trainable version of the classical k-Nearest Neighbour classifier (kNN) named kNN-Learning Vector Classifier. We develop the corresponding training formulas for the whole resulting structure and apply it to some classification benchmark problems. The experimental results give evidence of the nearly systematic advantage of our proposal with respect to MLPs, as well as of their competitive performance regarding the Modular Neural Networks (MNNs), which have a similar philosophy as our approach.es
dc.language.isoenes
dc.subjectTelecomunicacioneses
dc.titleCreating Modular-like Ensembles by Output Clusteringes
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subject.unesco3325 Tecnología de las Telecomunicacioneses
dc.description.departamentoTeoría de la Señal y Comunicaciones


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