# How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms

@article{Wainer2021HowTT,
title={How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms},
author={Jacques Wainer and Pablo Fonseca},
journal={Artif. Intell. Rev.},
year={2021},
volume={54},
pages={4771-4797}
}
• Published 26 August 2020
• Computer Science
• Artif. Intell. Rev.
SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters $C$ and $\gamma$ to the data itself. In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been proposed to solve it, among them: grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, Nelder Mead, and others. There have also been proposals to…
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