How far you can get using machine learning black-boxes

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorRocha, Anderson-
Autor(es): dc.creatorPapa, João Paulo-
Autor(es): dc.creatorMeira, Luis A. A.-
Data de aceite: dc.date.accessioned2025-08-21T20:54:10Z-
Data de disponibilização: dc.date.available2025-08-21T20:54:10Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2010-12-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI.2010.34-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/226275-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/226275-
Descrição: dc.descriptionSupervised Learning (SL) is a machine learning research area which aims at developing techniques able to take advantage from labeled training samples to make decisions over unseen examples. Recently, a lot of tools have been presented in order to perform machine learning in a more straightforward and transparent manner. However, one problem that is increasingly present in most of the SL problems being solved is that, sometimes, researchers do not completely understand what supervised learning is and, more often than not, publish results using machine learning black-boxes. In this paper, we shed light over the use of machine learning black-boxes and show researchers how far they can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. Here, we focus on one aspect of classifiers namely the way they compare examples in the feature space and show how a simple knowledge about the classifier's machinery can lift the results way beyond out-of-the-box machine learning solutions. © 2010 IEEE.-
Descrição: dc.descriptionInstitute of Computing Univ. of Campinas (UNICAMP), Campinas-
Descrição: dc.descriptionDepartment of Computer Science State Univ. of São Paulo (UNESP), Bauru-
Descrição: dc.descriptionDepartment of Science and Technology Federal Univ. of São Paulo (UNIFESP) São José Dos Campos-
Descrição: dc.descriptionDepartment of Computer Science State Univ. of São Paulo (UNESP), Bauru-
Formato: dc.format193-200-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 23rd SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2010-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectK-Nearest neighbors-
Palavras-chave: dc.subjectMachine learning black-boxes-
Palavras-chave: dc.subjectMetrics space-
Palavras-chave: dc.subjectNeural networks-
Palavras-chave: dc.subjectOptimum-Path forest-
Palavras-chave: dc.subjectPattern analysis-
Palavras-chave: dc.subjectSupport vector machines-
Título: dc.titleHow far you can get using machine learning black-boxes-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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