Future trends in optimum-path forest classification
dc.contributor.author | Papa, João Paulo [UNESP] | |
dc.contributor.author | Falcão, Alexandre Xavier | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.contributor.institution | Universidade Estadual de Campinas (UNICAMP) | |
dc.date.accessioned | 2023-03-02T08:37:56Z | |
dc.date.available | 2023-03-02T08:37:56Z | |
dc.date.issued | 2022-01-24 | |
dc.description.abstract | In 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 | en |
dc.description.affiliation | UNESP - São Paulo State University School of Sciences | |
dc.description.affiliation | Department of Computing São Paulo State University | |
dc.description.affiliation | Institute of Computing University of Campinas (UNICAMP) Campinas | |
dc.description.affiliationUnesp | UNESP - São Paulo State University School of Sciences | |
dc.description.affiliationUnesp | Department of Computing São Paulo State University | |
dc.format.extent | 217-219 | |
dc.identifier | http://dx.doi.org/10.1016/B978-0-12-822688-9.00017-7 | |
dc.identifier.citation | Optimum-Path Forest: Theory, Algorithms, and Applications, p. 217-219. | |
dc.identifier.doi | 10.1016/B978-0-12-822688-9.00017-7 | |
dc.identifier.scopus | 2-s2.0-85134954561 | |
dc.identifier.uri | http://hdl.handle.net/11449/242086 | |
dc.language.iso | eng | |
dc.relation.ispartof | Optimum-Path Forest: Theory, Algorithms, and Applications | |
dc.source | Scopus | |
dc.subject | Clustering | |
dc.subject | Deep learning | |
dc.subject | Machine learning | |
dc.subject | Optimum-path forest | |
dc.subject | Supervised learning | |
dc.title | Future trends in optimum-path forest classification | en |
dc.type | Capítulo de livro | |
unesp.campus | Universidade Estadual Paulista (Unesp), Faculdade de Ciências, Bauru | pt |
unesp.department | Computação - FC | pt |