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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15858
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dc.contributor.authorMayhua López, Efraín-
dc.contributor.authorGómez Verdejo, Vanessa-
dc.contributor.authorFigueiras Vidal, Aníbal-
dc.date.accessioned2019-01-29T22:19:54Z-
dc.date.available2019-01-29T22:19:54Z-
dc.date.issued2015-
dc.identifier.issn15662535es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15858-
dc.description.abstractBoosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly classified. If common kernel Support Vector Machines (SVMs) are used as basic learners to construct a Real AdaBoost ensemble, the resulting ensemble can be easily compacted into a monolithic architecture by simply combining the weights that correspond to the same kernels when they appear in different learners, avoiding to increase the operation computational effort for the above potential advantage. This way, the performance advantage that boosting provides can be obtained for monolithic SVMs, i.e., without paying in classification computational effort because many learners are needed. However, SVMs are both stable and strong, and their use for boosting requires to unstabilize and to weaken them. Yet previous attempts in this direction show a moderate success. In this paper, we propose a combination of a new and appropriately designed subsampling process and an SVM algorithm which permits sparsity control to solve the difficulties in boosting SVMs for obtaining improved performance designs. Experimental results support the effectiveness of the approach, not only in performance, but also in compactness of the resulting classifiers, as well as that combining both design ideas is needed to arrive to these advantageous designs. © 2014 Elsevier B.V.All rights reserved.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherElsevieres_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84924807118&doi=10.1016%2fj.inffus.2014.10.005&partnerID=40&md5=d6871508ff8878cf2929a6a050dd3c42es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectAdaptive boostinges_PE
dc.subjectAlgorithmses_PE
dc.subjectDesignes_PE
dc.subjectIterative methodses_PE
dc.subjectLinear programminges_PE
dc.subjectStructure (composition)es_PE
dc.subjectBoosting algorithmes_PE
dc.subjectComputational effortes_PE
dc.subjectEnsemble classifierses_PE
dc.subjectMonolithic architecturees_PE
dc.subjectPerformance designes_PE
dc.subjectSubsamplinges_PE
dc.subjectSupport vector machine (SVMs)es_PE
dc.subjectSupport vector machine classifierses_PE
dc.subjectSupport vector machineses_PE
dc.titleA new boosting design of Support Vector Machine classifierses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.doi15662535es_PE
Appears in Collections:Artículos de investigación

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