A New Support Vector Machine Algorithm with Scalable Penalty Coefficients for Training Samples

摘要：

In this paper, a new method to determine the penalty coefficients for different samples for the support vector machine (SVM) algorithm was proposed. Sequential minimal optimization (SMO) was then used to solve the SVM problem. Simulation results from applying the proposed method to binary classification problems show that the generalization error of the proposed method was smaller than standard SVM algorithm in the cases that the sizes of binary sample training sets (1) were selected in proportion; (2) were the same; (3) were quite different.

关键词： Support vector machine Sequential minimal optimization Structural risk minimization

作者: Jian Zhang Jun-Zhong Zou Lan-Lan Chen Chunmei Wang Min Wang

作者单位: School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

会议类型: 国际会议

会议名称: The Second International Conference on Cognitive Neurodynamics--2009(第二届国际认知神经动力学会议)

会议地点: 杭州

会议语种:英文

页码: 647-652

在线出版日期: 2009-11-15（万方平台首次上网日期，不代表论文的发表时间）