SUPPORT VECTOR MACHINES BASED ON HYPER-BALL CLUSTERING
In this paper, in order to reduce the support vectors on a large scale data set, we train Support Vector machines which utilize the hyper-spheres as the training samples. By representing adjacent samples of the same class as hyper-spheres, the boundary location can be controlled both by the center and radius of the hyper-spheres. We demonstrate that the optimization problem in this condition can be solved easily only by revising initial conditions of Sequential Minimal Optimization (SMO) algorithm. Compared with previous algorithms on several data sets, the proposed algorithm is quite competitive in both the computational efficiency and the classification accuracy.
Pattern classification support vector machines Sequential Minimal Optimization clustering technology
YING-HUA HE KUN-LONG ZHANG
School of Computer Science and Technology, Tianjin University
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
840-844
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）