Width optimization of RBF kernels for binary classification of support vector machines : a density estimation-based approach.

dc.contributor.authorMenezes, Murilo V. F.
dc.contributor.authorTorres, Luiz Carlos Bambirra
dc.contributor.authorBraga, Antônio de Pádua
dc.date.accessioned2022-10-10T20:58:03Z
dc.date.available2022-10-10T20:58:03Z
dc.date.issued2019pt_BR
dc.description.abstractKernels are often used for modelling non-linear data, developing a main role in models like the SVM. The optimization of its parameters to better fit each dataset is a frequently faced challenge: A bad choice of kernel parameters often implies a poor model. This problem is usually worked out using exhaustive search approaches, such as cross-validation. These methods, however, do not take into account existent information on data arrangement. This paper proposes an alternative approach, based on density estimation. By making use of density estimation methods to analyze the dataset structure, it is proposed a function over the kernel parameters. This function can be used to choose the parameters that best suit the data.pt_BR
dc.identifier.citationMENEZES, M. V. F.; TORRES, L. C. B; BRAGA, A. P. Width optimization of RBF kernels for binary classification of support vector machines: A density estimation-based approach. Pattern Recognition Letters, v. 128, 2019. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0167865519302156>. Acesso em: 29 abr. 2022.pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.patrec.2019.08.001pt_BR
dc.identifier.issn0167-8655
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/15663
dc.identifier.uri2https://www.sciencedirect.com/science/article/pii/S0167865519302156pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.titleWidth optimization of RBF kernels for binary classification of support vector machines : a density estimation-based approach.pt_BR
dc.typeArtigo publicado em periodicopt_BR
Arquivos
Pacote Original
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
ARTIGO_WidthOptimizationRFB.pdf
Tamanho:
1.53 MB
Formato:
Adobe Portable Document Format
Descrição:
Licença do Pacote
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição: