Abstract
In least squares support vector (LS-SVM), the key challenge lies in the selection of free parameters such as kernel parameters and tradeoff parameter. However, when a large number of free parameters are involved in LS-SVM, the commonly used grid search method for model selection is intractable. In this paper, SLOO-MPS is proposed for tuning multiple parameters for LS-SVM to overcome this problem. This method is based on optimizing the smooth leave- one-out error via a gradient descent algorithm and feasible to compute. Extensive empirical comparisons confirm the feasibility and validation of the SLOO-MPS.
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Bo, L., Wang, L., Jiao, L. (2005). Multiple Parameter Selection for LS-SVM Using Smooth Leave-One-Out Error. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_136
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DOI: https://doi.org/10.1007/11427391_136
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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