Journal
NEUROCOMPUTING
Volume 64, Issue -, Pages 107-117Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2004.11.022
Keywords
support vector machines; model selection; evolutionary algorithms
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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. (c) 2004 Elsevier B.V. All rights reserved.
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