自适应误差惩罚支撑向量回归机

SVR with Adaptive Error Penalization

该文提出一种支撑向量回归机AEPSVR。它首先用ε-SVR求得一个近似的支撑向量回归函数,在此基础上,引入一种新自适应误差惩罚函数,通过迭代,得到鲁棒的支撑向量回归机。该方法因以ε-SVR为基础,故可以应用各种求解SVR的优化算法。实验表明,该支撑向量回归机AEPSVR能显著地降低离群点的影响,具有良好的泛化性能。

A novel support vector regression method AEPSVR is proposed in this paper. First, an approximate regression function is obtained using ε-SVR method, and then a new adaptive error penalization function is introduced to enhance the robust performance of SVR such that a robust support vector regression is derived. Because the proposed AEPSVR here is based on ε-SVR, so various optimization methods for SVR can be used. Experimental results show that the proposed AEPSVR can reduce the affect of outliers, and have the very good generalization capability.