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

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 γ to the data itself. In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been pro...

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Veröffentlicht in:	The Artificial intelligence review Jg. 54; H. 6; S. 4771 - 4797
Hauptverfasser:	Wainer, Jacques, Fonseca, Pablo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.08.2021 Springer Springer Nature B.V
Schlagworte:	Algorithms Analysis Artificial Intelligence Bayesian analysis Binary data Classification Computation Computational geometry Computer Science Convexity Data Datasets Expenditures Function words Mathematical optimization Methods Optimization Particle swarm optimization Search algorithms Searches and seizures Simulated annealing Trees Non-convex optimization algorithms Grid search SVM Hyperparameters Random search
ISSN:	0269-2821, 1573-7462
Online-Zugang:	Volltext
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