Show simple item record

dc.contributor.authorLangseth, Helge
dc.contributor.authorNielsen, Thomas D.
dc.contributor.authorPérez-Bernabé, Inmaculada
dc.contributor.authorSalmerón Cerdán, Antonio
dc.date.accessioned2017-07-07T07:17:21Z
dc.date.available2017-07-07T07:17:21Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10835/4894
dc.description.abstractIn this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing a kernel density representation of the data, the estimation method relies on the specification of a kernel bandwidth. We show that in most cases the method is robust wrt. the choice of bandwidth, but for certain data sets the bandwidth has a strong impact on the result. Based on this observation, we propose an alternative learning method that relies on the cumulative distribution function of the data. Empirical results demonstrate the usefulness of the approaches: Even though the methods produce estimators that are slightly poorer than the state of the art (in terms of log-likelihood), they are significantly faster, and therefore indicate that the MoTBF framework can be used for inference and learning in reasonably sized domains. Furthermore, we show how a particular subclass of MoTBF potentials (learnable by the proposed methods) can be exploited to significantly reduce complexity during inference.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.subjectMixtures of truncated basis functionses_ES
dc.subjectHybrid Bayesian networkses_ES
dc.subjectLearninges_ES
dc.titleLearning Mixtures of Truncated Basis Functions from Dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doihttps://doi.org/10.1016/j.ijar.2013.09.012


Files in this item

This item appears in the following Collection(s)

Show simple item record

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