Theoretical background and related works

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.creatorAfonso, Luis C.S.-
Autor(es): dc.creatorFalcão, Alexandre Xavier-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T23:00:12Z-
Data de disponibilização: dc.date.available2025-08-21T23:00:12Z-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2022-01-23-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/B978-0-12-822688-9.00010-4-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/242083-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/242083-
Descrição: dc.descriptionThe Optimum-Path Forest (OPF) is a framework for the design of graph-based classifiers, which covers supervised, semisupervised, and unsupervised applications. The OPF is mainly characterized by its low training and classification times as well as competitive results against well-established machine learning techniques, such as Support Vector Machine and Artificial Neural Networks. Besides, the framework allows the design of different approaches based on the problem itself, which means a specific OPF-based classifier can be built for a given particular task. This paper surveyed several works published in the past years concerning OPF-based classifiers and sheds light on future trends concerning such a framework in the context of the deep learning era. © 2022 Copyright-
Descrição: dc.descriptionUNESP - São Paulo State University School of Sciences-
Descrição: dc.descriptionInstitute of Computing University of Campinas (UNICAMP) Campinas-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionUNESP - São Paulo State University School of Sciences-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Formato: dc.format5-54-
Idioma: dc.languageen-
Relação: dc.relationOptimum-Path Forest: Theory, Algorithms, and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectImage-forest transform-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectOptimum-path forest-
Palavras-chave: dc.subjectPattern recognition-
Título: dc.titleTheoretical background and related works-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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