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dc.contributor.authorBorchani, Hanen
dc.contributor.authorMartínez, Ana M.
dc.contributor.authorMasegosa, Andrés R.
dc.contributor.authorLangseth, Helge
dc.contributor.authorNielsen, Thomas D.
dc.contributor.authorSalmerón Cerdán, Antonio
dc.contributor.authorFernández, Antonio
dc.contributor.authorMadsen, Anders L.
dc.contributor.authorSáez, Ramón
dc.date.accessioned2017-06-16T08:22:52Z
dc.date.available2017-06-16T08:22:52Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10835/4861
dc.description.abstractAn often used approach for detecting and adapting to concept drift when doing classi cation is to treat the data as i.i.d. and use changes in classi cation accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure effcient inference and learning, we resort to a variational Bayes inference scheme. As a proof of concept, we demonstrate and analyze the proposed framework using synthetic data sets as well as a real fi nancial data set from a Spanish bank.es_ES
dc.language.isoenes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleModeling concept drift: A probabilistic graphical model based approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional