中文摘要：

基于最小二乘支持向量机回归算法,本文在前期工作的基础上进行了扩展,提出了更加详尽的自适应迭代最小二乘支持向量机回归算法.与标准的LSSVR相比,本文提出的算法在学习新样本的时候利用了已有的学习结果,可以快速获得新的学习机.模拟结果表明,自适应迭代最小二乘支持向量机回归算法能够自适应地确定支持向量的数目,保留了QP方法在训练SVM时支持向量的稀疏性,在相近的回归精度下,该算法极大地提高了标准LSSVR学习的速度.

英文摘要：

A novel adaptive and iterative training algorithm of least square support vector machine regression（AILSSVR） is presented based on the least square support vector machine regression（LSSVR） and the proof for inverse of an order-reduced matrix is given.Compared to the standard LSSVR,the proposed algorithm takes advantage of the previous training results when learning from a new sample;therefore,it can obtain a new learning machine efficiently.Numerical simulations demonstrate that AILSSVR can adaptively decide the number of support vectors and preserve the sparse property of support vectors in QP training methods.Under the similar regression accuracy,AILSSVR can speed up the learning process remarkably.