Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/81654
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dc.creatorJoão Paulo Papa
dc.creatorWillian Paraguassu Amorim
dc.creatorAlexandre Xavier Falcão
dc.creatorJoão Manuel R. S. Tavares
dc.date.accessioned2025-09-30T23:33:09Z-
dc.date.available2025-09-30T23:33:09Z-
dc.date.issued2015
dc.identifier.othersigarra:107756
dc.identifier.urihttps://hdl.handle.net/10216/81654-
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. (c) 2016 by World Scientific Publishing Co. Pte. Ltd.
dc.language.isoeng
dc.relation.ispartofHandbook Of Pattern Recognition And Computer Vision (5th Edition)
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectCiências Tecnológicas, Ciências da engenharia e tecnologias
dc.subjectTechnological sciences, Engineering and technology
dc.titleRecent advances on optimum-path forest for data classification: Supervised, semi-supervised, and unsupervised learning
dc.typeCapítulo ou Parte de Livro
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.doi10.1142/9789814656535_0006
dc.identifier.authenticusP-00V-MVV
dc.subject.fosCiências da engenharia e tecnologias
dc.subject.fosEngineering and technology
Appears in Collections:FEUP - Capítulo ou Parte de Livro

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