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2004

Evolutionary tuning of multiple SVM parameters

9 years 11 months ago
Evolutionary tuning of multiple SVM parameters
The problem of model selection for support vector machines (SVMs) is considered. We propose an evolutionary approach to determine multiple SVM hyperparameters: The covariance matrix adaptation evolution strategy (CMA-ES) is used to determine the kernel from a parameterized kernel space and to control the regularization. Our method is applicable to optimize non-differentiable kernel functions and arbitrary model selection criteria. We demonstrate on benchmark datasets that the CMA-ES improves the results achieved by grid search already when applied to few hyperparameters. Further, we show that the CMA-ES is able to handle much more kernel parameters compared to grid-search and that tuning of the scaling and the rotation of Gaussian kernels can lead to better results in comparison to standard Gaussian kernels with a single bandwidth parameter. In particular, more flexibility of the kernel can reduce the number of support vectors. Key words: support vector machines, model selection, evol...
Frauke Friedrichs, Christian Igel
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Frauke Friedrichs, Christian Igel
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