Share Email Print
cover

Proceedings Paper

Optimization of support vector machine hyperparameters by using genetic algorithm
Author(s): Zbigniew Szymanski; Stanislaw Jankowski; Dominik Grelow
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 26 April 2006
PDF: 6 pages
Proc. SPIE 6159, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, 615931 (26 April 2006); doi: 10.1117/12.674867
Show Author Affiliations
Zbigniew Szymanski, Warsaw Univ. of Technology (Poland)
Stanislaw Jankowski, Warsaw Univ. of Technology (Poland)
Dominik Grelow, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 6159:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV
Ryszard S. Romaniuk, Editor(s)

© SPIE. Terms of Use
Back to Top