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Learning radial basis neural networks in a lazy way: A comparative study

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorValls, José M.
dc.contributor.authorGalván, Inés M.
dc.contributor.authorIsasi, Pedro
dc.date.accessioned2009-04-13T13:41:52Z
dc.date.available2009-04-13T13:41:52Z
dc.date.issued2008-05
dc.description.abstractLazy learning methods have been used to deal with problems in which the learning examples are not evenly distributed in the input space. They are based on the selection of a subset of training patterns when a new query is received. Usually, that selection is based on the k closest neighbors and it is a static selection, because the number of patterns selected does not depend on the input space region in which the new query is placed. In this paper, a lazy strategy is applied to train radial basis neural networks. That strategy incorporates a dynamic selection of patterns, and that selection is based on two different kernel functions, the Gaussian and the inverse function. This lazy learning method is compared with the classical lazy machine learning methods and with eagerly trained radial basis neural networks.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationNeurocomputing, 2008, vol. 71, n. 13-15, p. 2529–2537
dc.identifier.doi10.1016/j.neucom.2007.10.030
dc.identifier.issn0925-2312
dc.identifier.publicationfirstpage2529
dc.identifier.publicationissue13-15
dc.identifier.publicationlastpage2537
dc.identifier.publicationtitleNeurocomputing
dc.identifier.publicationvolume71
dc.identifier.urihttps://hdl.handle.net/10016/3923
dc.language.isoeng
dc.publisherElsevier
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.neucom.2007.10.030
dc.rights© Elsevier
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherLazy learning
dc.subject.otherLocal learning
dc.subject.otherRadial basis neural networks
dc.subject.otherPattern selection
dc.titleLearning radial basis neural networks in a lazy way: A comparative study
dc.typeresearch article*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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