Artículo
Support vector machines for classification of input vectors with different metrics
Autor/es | González Abril, Luis
Velasco Morente, Francisco Ortega Ramírez, Juan Antonio Franco Martín, Luis |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Economía Aplicada I |
Fecha de publicación | 2011-05 |
Fecha de depósito | 2023-02-24 |
Publicado en |
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Resumen | In this paper, a generalization of support vector machines is explored where it is considered that input vectors have different ℓp norms for each class. It is proved that the optimization problem for binary classification ... In this paper, a generalization of support vector machines is explored where it is considered that input vectors have different ℓp norms for each class. It is proved that the optimization problem for binary classification by using the maximal margin principle with ℓp and ℓq norms only depends on the ℓp norm if 1 ≤ p ≤ q. Furthermore, the selection of a different bias in the classifier function is a consequence of the ℓq norm in this approach. Some commentaries on the most commonly used approaches of SVM are also given as particular cases. |
Cita | González Abril, L., Velasco Morente, F., Ortega Ramírez, J.A. y Franco Martín, L. (2011). Support vector machines for classification of input vectors with different metrics. Computers & Mathematics with Applications, 61 (9), 2874-2878. https://doi.org/10.1016/j.camwa.2011.03.071. |
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