Distributed mixture-of-experts for Big Data using PETUUM framework

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datacite.alternateIdentifier.citationProceedings - International Conference of the Chilean Computer Science Society, SCCC, 1-7, 2017
datacite.alternateIdentifier.doi10.1109/SCCC.2017.8405113es_ES
datacite.creatorPeralta, Billy
datacite.creatorParra, Luis
datacite.creatorHerrera, Oriel
datacite.creatorCaro, Luis
datacite.date2017
datacite.date.issued2019-09-05
datacite.subjectInvestigación conductuales_ES
datacite.subjectManejo de Datoses_ES
datacite.subjectAlgoritmos de Aprendizajees_ES
datacite.subjectBig Dataes_ES
datacite.titleDistributed mixture-of-experts for Big Data using PETUUM frameworkes_ES
dc.date.accessioned2019-09-05T20:51:47Z
dc.date.available2019-09-05T20:51:47Z
dc.description.abstractToday, organizations are beginning to realize the importance of using as much data as possible for decision-making in their strategy. The finding of relevant patterns in enormous amount of data requires automatic machine learning algorithms, among them, a popular option is the mixture-of-experts that allows to model data using a set of local experts. The problem of using typical learning algorithms over Big Data is the handling of these large datasets in primary memory. In this paper, we propose a methodology to learn a mixture-of-experts in a distributed way using PETUUM platform. Particularly, we propose to learn the parameters of mixture-of-experts by adapting the standard stochastic gradient descent in a distributed way. This methodology is applied to people detection with standard real datasets considering accuracy and precision metrics among other. The results show a consistent performance of mixture-of-experts models where the best number of experts varies according to the particular dataset. We also evidence the advantages of the distributed approach by showing the almost linear decreasing of average training time according to the number of processors. In a future work, we expect to apply this methodology to mixture-of-experts with embedded variable selection.es_ES
dc.formatPDFes_ES
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/1998
dc.language.isoenes_ES
dc.sourceProceedings - International Conference of the Chilean Computer Science Society, SCCCes_ES
oaire.resourceTypeArtículo de Revistaes_ES
uct.catalogadormlmes_ES
uct.indizacionSCOPUSes_ES
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