摘要
为了进一步提高核向量回归算法用于大样本回归问题的训练速度,提出了一种改进的核向量回归算法。该算法利用样本数据在特征空间中的映射点确定包围球半径,并使该半径在迭代过程中保持不变。通过缩小核心数据集,提高了回归算法的训练速度。对几组回归时间序列预测的仿真实验表明,改进的核向量回归算法的训练时间和支持向量的数目均小于核向量回归算法,但二者具有相似的回归精度,从而验证了改进的核向量回归算法的有效性。
To further increase the training speed of a core vector regression algorithm for large samples, an improved core vector regression algorithm is proposed. This algorithm uses the mapping points of sample data in feature space to obtain the radius of the enclosing ball and makes the radius keep unchanged in the iterative process. By reducing the size of core data set, the training speed is increased. Simulations on prediction of several sets of time series show that on maintaining the same regression accuracy as the core vector regression algorithm, the improved core vector regression algorithm enjoys both less training time and fewer support vectors, so that its efficiency is proved.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第12期2968-2972,共5页
Systems Engineering and Electronics
关键词
回归算法
核向量回归
大样本训练
最小包围球
regression algorithm
core vector regression
large sample training
the smallest enclosing ball
