Future trends in optimum-path forest classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.creatorPapa, João Paulo-
Autor(es): dc.creatorFalcão, Alexandre Xavier-
Data de aceite: dc.date.accessioned2025-08-21T16:06:40Z-
Data de disponibilização: dc.date.available2025-08-21T16:06:40Z-
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/B978-0-12-822688-9.00017-7-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/242086-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/242086-
Descrição: dc.descriptionIn the past years, we have observed an increasing number of applications that require machine learning techniques to sort out problems that are not straightforward to humans. The reasons vary from information that is not clearly visible to the human eye (e.g., microscopic patterns in medical images) or the massive amount of data to analyze. This book aimed to shed light on the Optimum-Path Forest framework, which comprises approaches to dealing with supervised, semi-supervised, and unsupervised learning. Different applications have been presented together with a theoretical background concerning the techniques presented here. We expect to call the attention and curiosity of the readers towards OPF-based techniques and their strengths. © 2022 Copyright-
Descrição: dc.descriptionUNESP - São Paulo State University School of Sciences-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionInstitute of Computing University of Campinas (UNICAMP) Campinas-
Descrição: dc.descriptionUNESP - São Paulo State University School of Sciences-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Formato: dc.format217-219-
Idioma: dc.languageen-
Relação: dc.relationOptimum-Path Forest: Theory, Algorithms, and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClustering-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectOptimum-path forest-
Palavras-chave: dc.subjectSupervised learning-
Título: dc.titleFuture trends in optimum-path forest classification-
Tipo de arquivo: dc.typelivro digital-
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