Parameter Selection for Gaussian Radial Basis Function in Support Vector Machine Classification

摘要：

　　The Gaussian radial basis function is widely used in the support vector machine (SVM) due to its attractive characteristics.The parameter (σ) in this kernel is crucial to robust performance of SVM.In this paper,we derive a formula to compute the optimal σ under the principle of maximizing the class separability in the kernel space.The most attractive feature of the proposed method is that no optimization search algorithm is required in parameter selection;and thus our method is computational effective.The experimental results demonstrate the proposed method is fast and robust.

关键词： parameter selection model selection Gaussian radial basis function support vector machine class separability

作者: Zhiliang Liu Ming J. Zuo Hongbing Xu

作者单位: School of Automation Engineering, University of Electronic Science and Technology of China Chengdu, School of Mechatronic Engineering University of Electronic Science and Technology of China Chengdu, School of Automation Engineering University of Electronic Science and Technology of China Chengdu, 6

会议类型: 国际会议

会议名称: 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering & The 3rd International Conference on Maintenance Engineering (2012质量,可靠性,风险,维修性及安全性工程国际会议(QR2MSE 2012 & ICME 2012))

会议地点: 成都

会议语种:英文

页码: 574-580

在线出版日期: 2012-06-15（万方平台首次上网日期，不代表论文的发表时间）