A Kernel-Based Weight-Setting Method in Robust Weighted Least Squares Support Vector Regression

By combining the basic idea of weighted least squares support vector machines (WLS-SVM) and fuzzy support vector machines (FSVM), a weight-setting strategy based on 2-norm distance and neighborhood density (WLS-SVM I) is presented in this paper. Then the relationship between the 2-norm distance and RBF kernel is revealed. Consequently, an equivalent weight setting strategy (WLS-SVM II) using information from RBF kernel is put forward. Numerical experiments show both the 2-norm distance-based strategy and the kernel-based strategy produce robust LS-SVM estimators of noisy data. And when satisfying some conditions, WLS-SVM I can be substituted by WLS-SVM II, which may provide an efficiency-enhancing strategy for online LS-SVM