@inproceedings{2ede1708cdec4c94a5006c2bb409c880,
title = "Multi-parametric Gaussian kernel function optimization for ε-SVMr using a genetic algorithm",
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.",
author = "J. Gasc{\'o}n-Moreno and Ortiz-Garc{\'i}a, {E. G.} and S. Salcedo-Sanz and A. Paniagua-Tineo and B. Saavedra-Moreno and Portilla-Figueras, {J. A.}",
year = "2011",
doi = "10.1007/978-3-642-21498-1_15",
language = "English (US)",
isbn = "9783642214974",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "113--120",
booktitle = "Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings",
edition = "PART 2",
note = "11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 ; Conference date: 08-06-2011 Through 10-06-2011",
}