A Proposal for Mixture of Experts with Entropic Regularization

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dc.contributor.authorPeralta Márquez, Billy
dc.contributor.authorSaavedra, Ariel
dc.contributor.authorCaro Saldivia, Luis
dc.contributor.authorMonteverde, H
dc.contributor.authorSantos, R
dc.date2017
dc.date.accessioned2021-04-30T16:35:24Z
dc.date.available2021-04-30T16:35:24Z
dc.description.abstractIn these days, there are a growing interest in pattern recognition for tasks as prediction of weather events, recommendation of the best route, intrusion detection or face detection. Each of these tasks can be modelled as classification problem, where a common alternative is to use an ensemble model of classification. A well-known example is given by Mixture-of-Experts model, which represents a probabilistic artificial neural network consisting of local experts classifiers weighted by a gate network, and whose combination creates an environment of competition among experts seeking to obtain patterns of the data source. We observe that this architecture assume that one gate influence only one data point, consequently the training can be misguided in real datasets where the data is better explained by multiple experts. In this work, we present a variant of regular Mixture-of-Experts model, which consists of maximizing of the entropy of gate network in addition to classification cost minimization. The results show the advantage of our approach in multiple datasets in terms of accuracy metric. As a future work, we plan to apply this idea to the Mixture-of-Experts with embedded feature selection.
dc.identifier.citation2017 XLIII LATIN AMERICAN COMPUTER CONFERENCE (CLEI),Vol.,,2017
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/3188
dc.language.isoen
dc.publisherIEEE
dc.source2017 XLIII LATIN AMERICAN COMPUTER CONFERENCE (CLEI)
dc.subject.englishmixture-of-experts
dc.subject.englishregularization
dc.subject.englishclassification
dc.titleA Proposal for Mixture of Experts with Entropic Regularization
dc.typeMeeting
uct.catalogadorWOS
uct.indizacionISTP
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