增量回归支持向量机改进学习算法  

Incremental Regressive Learning Algorithm of Support Vector Machine

作　　者：张仕华[1] 王学业[2] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105 [2]湘潭大学化学学院,湖南湘潭411105

出　　处：《计算机工程与应用》2006年第3期40-42,105,共4页Computer Engineering and Applications

基　　金：湖南省中青年科技基金资助项目(编号:01JZY2099)

摘　　要：传统的支持向量机不具有增量学习性能,而常用的增量学习方法具有不同的优缺点,为了解决这些问题,提高支持向量机的训练速度,文章分析了支持向量机的本质特征,根据支持向量机回归仅与支持向量有关的特点,提出了一种适合于支持向量机增量学习的算法(IRSVM),提高了支持向量机的训练速度和大样本学习的能力,而支持向量机的回归能力基本不受影响,取得了较好的效果。There is no incremental learning ability for the traditional support vector machine and there are all kind of merits and flaws for usually used incremental learning method.Nornlal SVM is unable to train in large-scale samples, while the computer＇s memory is too small.In order to resolve this problem and improve training speed of the SVM,we analyze essential characteristic of SVM and bring up the incremental learning algorithm of SVM based on regression of SVM related to SV（Support Vectors）.The algorithm increases the speed of training and the ability of learning with largescale samples while its regressive precision loses fewer.The experiments show that SVM performs effectively and practically.

关 键 词：增量学习 支持向量机 回归估计 IRSVM 大样本学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]