Recent advances on optimum-path forest for data classification: Supervised, semi-supervised, and unsupervised learning

dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorAmorim, Willian Paraguassu
dc.contributor.authorFalcão, Alexandre Xavier
dc.contributor.authorTavares, João Manuel R.S.
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionFederal University of Dourados Region
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade do Porto
dc.date.accessioned2022-05-01T09:47:30Z
dc.date.available2022-05-01T09:47:30Z
dc.date.issued2015-12-15
dc.description.abstractAlthough one can find several pattern recognition techniques out there, there is still room for improvements and new approaches. In this book chapter, we revisited the Optimum-Path Forest (OPF) classifier, which has been evaluated over the last years in a number of applications that consider supervised, semi-supervised and unsupervised learning problems. We also presented a brief compilation of a number of previous works that employed OPF in different research fields, that range from remote sensing image classification to medical data analysis.en
dc.description.affiliationDepartment of Computing São Paulo State University
dc.description.affiliationFederal University of Dourados Region
dc.description.affiliationInstitute of Computing University of Campinas
dc.description.affiliationInstituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial Departamento e Engenharia Mecânica Faculdade de Engenharia Universidade do Porto
dc.description.affiliationUnespDepartment of Computing São Paulo State University
dc.format.extent109-123
dc.identifierhttp://dx.doi.org/10.1142/9789814656535_0006
dc.identifier.citationHandbook Of Pattern Recognition And Computer Vision (5th Edition), p. 109-123.
dc.identifier.doi10.1142/9789814656535_0006
dc.identifier.scopus2-s2.0-85118019426
dc.identifier.urihttp://hdl.handle.net/11449/233744
dc.language.isoeng
dc.relation.ispartofHandbook Of Pattern Recognition And Computer Vision (5th Edition)
dc.sourceScopus
dc.titleRecent advances on optimum-path forest for data classification: Supervised, semi-supervised, and unsupervised learningen
dc.typeCapítulo de livro
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

Arquivos