Abstract
In this paper we propose a novel multi-parametric kernel Support Vector Regression algorithm optimized with a genetic algorithm. The multi-parametric model and the genetic algorithm proposed are both described with detail in the paper. We also present experimental evidences of the good performance of the genetic algorithm, when compared to a standard Grid Search approach. Specifically, results in different real regression problems from public repositories have shown the good performance of the multi-parametric kernel approach both in accuracy and computation time.
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Gascón-Moreno, J., Ortiz-García, E.G., Salcedo-Sanz, S., Paniagua-Tineo, A., Saavedra-Moreno, B., Portilla-Figueras, J.A. (2011). Multi-parametric Gaussian Kernel Function Optimization for ε-SVMr Using a Genetic Algorithm. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_15
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DOI: https://doi.org/10.1007/978-3-642-21498-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21497-4
Online ISBN: 978-3-642-21498-1
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