Authors
J Gascón-Moreno, Emilio G Ortiz-García, Sancho Salcedo-Sanz, A Paniagua-Tineo, B Saavedra-Moreno, José Antonio Portilla-Figueras
Publication date
2011
Conference
Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part II 11
Pages
113-120
Publisher
Springer Berlin Heidelberg
Description
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.
Scholar articles
J Gascón-Moreno, EG Ortiz-García, S Salcedo-Sanz… - … in Computational Intelligence: 11th International Work …, 2011