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: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2012 |
| País: | Colombia |
| Recursos: | Universidad Nacional de Colombia |
| Repositório: | Repositorio UN |
| Idioma: | espanhol |
| OAI Identifier: | oai:repositorio.unal.edu.co:unal/31045 |
| Acesso em linha: | https://repositorio.unal.edu.co/handle/unal/31045 http://bdigital.unal.edu.co/21121/ |
| Access Level: | Acceso aberto |
| Palavra-chave: | Informatics Electrical and Electronic Engineering Parameter selection Least Squares-Support Vector Machines Multidimensional Generalized Cross Validation Regression. |
| Resumo: | 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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