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dc.contributor.authorFlesch, Ildikó
dc.contributor.authorFernández, Antonio
dc.contributor.authorSalmerón Cerdán, Antonio
dc.date.accessioned2012-05-28T08:38:33Z
dc.date.available2012-05-28T08:38:33Z
dc.date.issued2007
dc.identifier.citationProceedings of the CEDI'07-SICO'07, pp. 217-224.es_ES
dc.identifier.urihttp://hdl.handle.net/10835/1548
dc.description.abstractIn this paper we propose an incremental method for building classifiers in domains with very large amounts of data or for data streams. The method is based on the use of mixtures of truncated exponentials, so that continuous and discrete variables can be handled simultaneously.es_ES
dc.language.isoenes_ES
dc.sourceSimposio nacional de Inteligencia Computacional 2007es_ES
dc.titleIncremental supervised classification for the MTE distribution: a preliminary studyes_ES
dc.typeinfo:eu-repo/semantics/reportes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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