Recent advances on optimum-path forest for data classification: Supervised, semi-supervised, and unsupervised learning
| dc.contributor.author | Papa, João Paulo [UNESP] | |
| dc.contributor.author | Amorim, Willian Paraguassu | |
| dc.contributor.author | Falcão, Alexandre Xavier | |
| dc.contributor.author | Tavares, João Manuel R.S. | |
| dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
| dc.contributor.institution | Federal University of Dourados Region | |
| dc.contributor.institution | Universidade Estadual de Campinas (UNICAMP) | |
| dc.contributor.institution | Universidade do Porto | |
| dc.date.accessioned | 2022-05-01T09:47:30Z | |
| dc.date.available | 2022-05-01T09:47:30Z | |
| dc.date.issued | 2015-12-15 | |
| dc.description.abstract | Although 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.affiliation | Department of Computing São Paulo State University | |
| dc.description.affiliation | Federal University of Dourados Region | |
| dc.description.affiliation | Institute of Computing University of Campinas | |
| dc.description.affiliation | Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial Departamento e Engenharia Mecânica Faculdade de Engenharia Universidade do Porto | |
| dc.description.affiliationUnesp | Department of Computing São Paulo State University | |
| dc.format.extent | 109-123 | |
| dc.identifier | http://dx.doi.org/10.1142/9789814656535_0006 | |
| dc.identifier.citation | Handbook Of Pattern Recognition And Computer Vision (5th Edition), p. 109-123. | |
| dc.identifier.doi | 10.1142/9789814656535_0006 | |
| dc.identifier.scopus | 2-s2.0-85118019426 | |
| dc.identifier.uri | http://hdl.handle.net/11449/233744 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Handbook Of Pattern Recognition And Computer Vision (5th Edition) | |
| dc.source | Scopus | |
| dc.title | Recent advances on optimum-path forest for data classification: Supervised, semi-supervised, and unsupervised learning | en |
| dc.type | Capítulo de livro | pt |
| dspace.entity.type | Publication | |
| relation.isDepartmentOfPublication | 872c0bbb-bf84-404e-9ca7-f87a0fe94e58 | |
| relation.isDepartmentOfPublication.latestForDiscovery | 872c0bbb-bf84-404e-9ca7-f87a0fe94e58 | |
| relation.isOrgUnitOfPublication | aef1f5df-a00f-45f4-b366-6926b097829b | |
| relation.isOrgUnitOfPublication.latestForDiscovery | aef1f5df-a00f-45f4-b366-6926b097829b | |
| unesp.campus | Universidade Estadual Paulista (UNESP), Faculdade de Ciências, Bauru | pt |
| unesp.department | Computação - FC | pt |

