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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15808
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dc.contributor.authorCulquicondor, Aldo-
dc.contributor.authorCastelo Fernández, Cesar-
dc.contributor.authorPaulo Papa, Joao-
dc.date.accessioned2019-01-29T22:19:51Z-
dc.date.available2019-01-29T22:19:51Z-
dc.date.issued2017-
dc.identifier.isbn9783319522760es_PE
dc.identifier.issn3029743es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15808-
dc.description.abstractIn this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness. © Springer International Publishing AG 2017.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherSpringer Verlages_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85013418925&doi=10.1007%2f978-3-319-52277-7_24&partnerID=40&md5=b9c9313fb37394b9f9e08b705310a884es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectEconomic and social effectses_PE
dc.subjectForestryes_PE
dc.subjectParallel algorithmses_PE
dc.subjectGraph algorithmses_PE
dc.subjectOptimum-path forestses_PE
dc.subjectParallel traininges_PE
dc.subjectParallelizinges_PE
dc.subjectSpeed upes_PE
dc.subjectTrade offes_PE
dc.subjectTraining phasees_PE
dc.subjectPattern recognitiones_PE
dc.titleA new parallel training algorithm for optimum-path forest-based learninges_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.doi10.1007/978-3-319-52277-7_24es_PE
Appears in Collections:Artículos de investigación

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