CAJ | 학술논문

针对二乘向量机(LS-SVM)对所有样本误差惩罚相同、预测精度不高的问题,提出了一种基于AdaBoost模型的二乘向量回归机.该算法使用多个二乘向量机按照某种学习规则协调各二乘向量机的输出,同时根据回归精度,建立各二乘向量机中每一个样本的误差惩罚权重,以突出样本的惩罚差异性,提高算法的泛化性能.实验结果表明,提出的算法提高了二乘向量回归机的预测精度,优化了学习机的性能.
침대이승향량궤(LS-SVM)대소유양본오차징벌상동、예측정도불고적문제,제출료일충기우AdaBoost모형적이승향량회귀궤.해산법사용다개이승향량궤안조모충학습규칙협조각이승향량궤적수출,동시근거회귀정도,건립각이승향량궤중매일개양본적오차징벌권중,이돌출양본적징벌차이성,제고산법적범화성능.실험결과표명,제출적산법제고료이승향량회귀궤적예측정도,우화료학습궤적성능.
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.