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...
- 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
| Summary: | 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 not require a prior knowledge about the influence of the LS-SVM free parameters in the results. The methodology is tested on two artificial and two real-world data sets. According to the results our methodology computes suitable regressions with competitive relative errors. |
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