Training optimum-path forest on graphics processing units

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorIwashita, Adriana S.-
Autor(es): dc.creatorRomero, Marcos V.T.-
Autor(es): dc.creatorBaldassin, Alexandro-
Autor(es): dc.creatorCosta, Kelton A.P.-
Autor(es): dc.creatorPapa, João P.-
Data de aceite: dc.date.accessioned2025-08-21T23:39:53Z-
Data de disponibilização: dc.date.available2025-08-21T23:39:53Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2014-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0004737805810588-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/227857-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/227857-
Descrição: dc.descriptionIn this paper, we presented a Graphics Processing Unit (GPU)-based training algorithm for Optimum-Path Forest (OPF) classifier. The proposed approach employs the idea of a vector-matrix multiplication to speed up both traditional OPF training algorithm and a recently proposed Central Processing Unit (CPU)-based OPF training algorithm. Experiments in several public datasets have showed the efficiency of the proposed approach, which demonstrated to be up to 14 times faster for some datasets. To the best of our knowledge, this is the first GPU-based implementation for OPF training algorithm. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.-
Descrição: dc.descriptionDepartment of Computing São Paulo State University, Bauru, São Paulo-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computation São Paulo State University, Rio-Claro, São Paulo-
Descrição: dc.descriptionDepartment of Computing São Paulo State University, Bauru, São Paulo-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computation São Paulo State University, Rio-Claro, São Paulo-
Formato: dc.format581-588-
Idioma: dc.languageen-
Relação: dc.relationVISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications-
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Palavras-chave: dc.subjectGraphics Processing Unit-
Palavras-chave: dc.subjectOptimum-Path Forest-
Título: dc.titleTraining optimum-path forest on graphics processing units-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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