关键词: AdaBoost算法, 二乘向量机, 回归

Abstract: In the standard Least Squares Support Vector Machine (LS-SVM) for regression, every training sample is equa11y considered, which is unsuitable when there exists significant difference among the training samples. The weighted least squares support vector regression based on AdaBoost algorithm was proposed. Learning by a series of support vector regressions, the proposed approach combined all the results in accordance with some rule. At the same time, adaptive weighted factors in LS-SVM were constructed to control the error function according to the regression error. It emphasized the significant difference among the training samples by adaptive weighted factors and improved the performance of generalization error. The experimental results demonstrate that the proposed approach has a competitive learning ability and acquires better accuracy than LS-SVM.

Key words: AdaBoost algorithm, Least Squares Support Vector Machine (LS-SVM), regression