Multiple parameter selection for LS-SVM using smooth leave-one-out error

Authors:
Liefeng Bo;Ling Wang;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing and National Key Laboratory, for Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Laboratory, for Radar Signal Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing and National Key Laboratory, for Radar Signal Processing, Xidian University, Xi'an, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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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.