摘要
提出一种针对多样本的在线支持向量回归(SVR)算法,以解决目前SVR在线训练算法每次只能处理1个样本的问题.算法以拉格朗日乘数法和库恩-塔克(KKT)条件为基础,逐步改变样本的系数,并在每次迭代中保持原来的样本满足KKT条件,最终使所有训练样本满足KKT条件.实验表明,该方法可有效更新SVR模型,且计算效率相比于基于单样本的在线回归算法有较大的优势.
An on-line support vector regression(SVR) algorithm with multiple samples algorithm is proposed in order to overcome the drawbacks of the previously proposed algorithm which can only deal with one training sample at a time.The algorithm is based on the Lagrangian multiplier method and the KKT conditions.At each step of iteration,the algorithm modifies the Lagrangian multipliers of the updated samples while making sure the KKT conditions are fulfilled for all other training samples.The on-line training algorithm terminates when all training samples fulfill the KKT conditions.Experimental result shows that the proposed algorithm can update the SVR model on-line effectively and it is much more efficient than the on-line training algorithm with single samples.
出处
《浙江大学学报(理学版)》
CAS
CSCD
北大核心
2011年第4期405-408,共4页
Journal of Zhejiang University(Science Edition)
基金
国家自然科学基金资助项目(60720106003)
关键词
支持向量回归
增量学习
在线训练
support vector regression (SVR)
incremental learning
on-line training