Optimization of support vector machine hyperparameters by using genetic algorithm

Zbigniew Szymanski; Stanislaw Jankowski; Dominik Grelow

doi:10.1117/12.674867

26 April 2006 Optimization of support vector machine hyperparameters by using genetic algorithm

Zbigniew Szymanski, Stanislaw Jankowski, Dominik Grelow

Proceedings Volume 6159, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV; 615931 (2006) https://doi.org/10.1117/12.674867
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, 2005, Wilga, Poland

Abstract

Support vector machines with Gaussian kernel are used in classification tasks with linear non-separable data. The Gaussian kernel is parametrized by two values (hyperparameters): C,γ. Hyperparameters selection, also known as model selection, affects the generalization performance of classifier. Retaining high generalization performance is vital to achieving good prediction results on unknown datasets. There is no strict rule for proper model selection. The range of hyperparameters' values is wide, so this is a time consuming task in general. In our approach genetic algorithm is exploited to find optimal hyperparameters values.

Citation Download Citation

Zbigniew Szymanski, Stanislaw Jankowski, and Dominik Grelow "Optimization of support vector machine hyperparameters by using genetic algorithm", Proc. SPIE 6159, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, 615931 (26 April 2006); https://doi.org/10.1117/12.674867

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