Multiple Parameter Selection for LS-SVM Using Smooth Leave-One-Out Error

摘要

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.