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 not require 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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| Main Authors: | , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Universidad Nacional de Colombia
2012
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| Online Access: | http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532012000100003 |
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| 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 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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