Dynamic Support Vector Machine by Distributing Kernel Function

摘要：

A dynamic support vector machine by distributing kernel function is put forward by integrating the target feature with the SVM. It distributes different Gauss kernel function to each training sample by using the distance between the target feature and each training sample. It is trained after the dynamic set is reconstructed according to the distance between the target feature and each training sample. Experiment results show that it is more robust than the traditional SVM.

关键词： support vector machine (SVM) dynamic support vector machine Gauss kernel Junction target recognition

作者: SHI Guangzhi DA Lianglong HU Junchuan ZHOU Yanxia

作者单位: Navy Submarine Academy Qingdao, China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 362-365

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）