1. DSpace-CRIS UAEMEX
  2. Interoperabilidad Repositorio Nacional
  3. Producción
Please use this identifier to cite or link to this item: http://ri.uaemex.mx/handle20.500.11799/41184
DC FieldValueLanguage
dc.creatorJair Cervantes Canales-
dc.creatorFarid García Lamont-
dc.creatorASDRUBAL LOPEZ CHAU-
dc.creatorLisbeth Rodríguez Mazahua-
dc.creatorJOSE SERGIO RUIZ CASTILLA-
dc.date2015-08-18-
dc.identifierhttp://hdl.handle.net/20.500.11799/41184-
dc.descriptionSupport Vector Machine (SVM) has important properties such as a strong mathematical background and a better generalization capability with respect to other classification methods. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent on the size of input data set. In this study, a new algorithm to speed up the training time of SVM is presented; this method selects a small and representative amount of data from data sets to improve training time of SVM. The novel method uses an induction tree to reduce the training data set for SVM, producing a very fast and high-accuracy algorithm. According to the results, the proposed algorithm produces results with similar accuracy and in a faster way than the current SVM implementations.-
dc.descriptionProyecto UAEM 3771/2014/CI-
dc.languageeng-
dc.publisherApplied Soft Computing-
dc.relationdx.doi.org/10.1016/j.asoc.2015.08.048;-
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0-
dc.source1568-4946-
dc.subjectSVM-
dc.subjectClassification-
dc.subjectLarge data sets-
dc.subjectinfo:eu-repo/classification/cti/7-
dc.titleData selection based on decision tree for SVM classification on large data sets-
dc.typearticle-
dc.audiencestudents-
dc.audienceresearchers-
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:Producción
Show simple item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.