THE BEAT-WAVE SIGNAL REGRESSION BASED ON LEAST SQUARES REPRODUCING KERNEL SUPPORT VECTOR MACHINE

摘要：

The kernel function of support vector machine(SVM) is an important factor for studying the result of the SVM. Based on the conditions of the support vector kernel function and reproducing kernel(RK) theory, a novel notion of least squares RK support vector machine(LS-RKSVM) with a RK on the Sobolev Hilbert space H(R;a.b) is proposed for regressing Beat-wave signal. The choice of the RK is important in SVM technic. The RK function enhances the generalization ability of least squares support vector machine(LS-SVM) method. The simulation results are presented to illustrate the feasibility of the proposed method, this model gives a better experiment results.

关键词： SVM Kernel function Reproducing kernel Signal regression

作者: CAI-XIA DENG LI-XIANG XU ZUO-XIAN FU

作者单位: Applied Science College, Harbin University of Science and Technology, Harbin 150080, China Department of Mathmatics and physics, Hefei University, Hefei 230601, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3641-3645

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）