LINEAR PROGRAMMING APPROACH FOR THE INVERSE PROBLEM OF SUPPORT VECTOR MACHINES
It is well recognized that support vector machines (SVMs)would produce better classification performance in terms of generalization power. A SVM constructs an optimal separating hyper-plane through maximizing the margin between two classes in high-dimensional feature space. The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains the maximum. It is difficult to give an exact solution to this problem, so a genetic algorithm is designed to solve this problem. But the proposed genetic algorithm has large time complexity for the process of solving quadratic programs. In this paper, we replace the quadratic programming with a linear programming. The new algorithm can greatly decrease time complexity. The fast algorithm for acquiring the maximum margin can upgrade the applicability of the proposed genetic algorithm.
Support vector machines linear programming genetic algorithms maximum margin
QIANG HE XUE-JUN SONG GANG YANG
Faculty of Mathematics & Computer Science, Hebei University, Baoding, Hebei, China, 071002 Faculty of Physics Science & Information Engineering, Hebei Normal University, Shijiazhuang, Hebei,
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3519-3522
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）