Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation

In this work a new methodology for automatic selection of the free parameters in the Least Squares–Support Vector Machines (LS-SVM) regression oriented algorithm is proposed. We employ a multidimensional Generalized Cross-Validation analysis in the linear equation system of LS-SVM. Our approach does...

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Autores:
Álvarez-Meza, Andrés Marino
Daza Santacoloma, Genaro
Acosta Mejia, Carlos
Castallanos Dominguez, German
Tipo de recurso:
Article of journal
Fecha de publicación:
2012
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/31045
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/31045
http://bdigital.unal.edu.co/21121/
Palabra clave:
Informatics
Electrical and Electronic Engineering
Parameter selection
Least Squares-Support Vector Machines
Multidimensional Generalized Cross Validation
Regression.
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional