支持向量回归机预测误差校正方法

针对传统的ε不敏感支持向量回归机(ε insensitive support vector regression, ε-SVR)未充分考虑局部支持向量对回归预测结果的影响,不利于提高回归预测精度的问题,提出了一种εSVR预测误差校正方法。该方法以期望预测值与εSVR回归预测值及局部支持向量间的欧氏距离和最小为目标函数,以ε不敏感损失带(εtube)宽度为约束条件,通过利用高维特征空间中εtube边界上和边界外的局部支持向量对εSVR的回归预测值进行误差校正。利用人工产生的不同分布数据集和UCI数据集进行的仿真结果表明,与传统的εSVR相比,该文方法具有更高的预测精度和更强的泛化能力。

Error correction method for support vector regression

The influence of the local support vector on the prediction results is not fully considered in the traditional ε insensitive support vector regression (ε-SVR), which is not conducive to improve the predictive accuracy of regression problems. An error correction method is proposed for ε-SVR, in which the minimum sum of Euclidean distances between ideal values and ε-SVR regression values and local support vectors are taken as the objective function, and the width of εinsensitive loss tube (εtube) is taken as constraint to correct the error in terms of local support vector on and out of the ε tube boundary in high dimensional feature space. Simulation using artificial datasets with different distributed and UCI benchmark data sets shows that the proposed method has higher prediction and generalization performance.