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dc.creatorVarela, Noel
dc.creatorComas-González, Zoe
dc.creatorTernera-Muñoz, Yesith R
dc.creatorEsmeral-Romero, Ernesto F
dc.creatorLizardo Zelaya, Nelson Alberto
dc.date.accessioned2021-01-05T21:45:46Z
dc.date.available2021-01-05T21:45:46Z
dc.date.issued2020
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11323/7660
dc.description.abstractSince 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processing studies, within traditional and new fields, including key aspects of machine learning and artificial intelligence. This paper proposes an alternative scheme for training data management in CNNs, consisting of selective-adaptive data sampling. By means of experiments with the CIFAR10 database for image classification.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceProcedia Computer Sciencespa
dc.subjectDynamic trainingspa
dc.subjectDeep convolutional networksspa
dc.subjectImage classificationspa
dc.titleMethod for classifying images in databases through deep convolutional networksspa
dc.typearticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920317014spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.07.022


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