A mixed quadratic programming model for a robust support vector machine.

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorSerna Diaz, Raquel-
Autor(es): dc.creatorLeite, Raimundo Santos-
Autor(es): dc.creatorSilva, Paulo José da Silva e-
Data de aceite: dc.date.accessioned2025-08-21T15:44:50Z-
Data de disponibilização: dc.date.available2025-08-21T15:44:50Z-
Data de envio: dc.date.issued2023-02-05-
Data de envio: dc.date.issued2023-02-05-
Data de envio: dc.date.issued2020-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/jspui/handle/123456789/16117-
Fonte completa do material: dc.identifierhttps://dx.doi.org/10.17268/sel.mat.2021.01.03-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1023142-
Descrição: dc.descriptionSupport Vector Machines are extensively used to solve classification problems in Pattern Recognition. They deal with small errors in the training data using the concept of soft margin, that allow for imperfect classification. However, if the training data have systematic errors or outliers such strategy is not robust resulting in bad generalization. In this paper we present a model for robust Support Vector Machine classification that can automatically ignore spurius data. We show then that the model can be solved using a high performance Mixed Integer Quadratic Programming solver and present preliminary numerical experiments using real world data that looks promissing.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsaberto-
Direitos: dc.rightsThis work is licensed under the Creative Commons - Attribution 4.0 International (CC BY 4.0). Fonte: o PDF do artigo.-
Palavras-chave: dc.subjectMixed integer quadratic programming-
Palavras-chave: dc.subjectOutliers-
Palavras-chave: dc.subjectClassification-
Título: dc.titleA mixed quadratic programming model for a robust support vector machine.-
Aparece nas coleções:Repositório Institucional - UFOP

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