Quantum-inspired evolutionary tuning of SVM parameters |
| |
Authors: | Zhiyong Luo Ping Wang Yinguo Li Wenfeng Zhang Wei Tang Min Xiang |
| |
Institution: | [1]School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; [2]School of Automation, Northwestern Polytechnical University, Xfan 710072, China |
| |
Abstract: | The most commonly used parameters selection method for support vector machines (SVM) is cross-validation, which needs a longtime complicated calculation. In this paper, a novel regularization parameter and a kernel parameter tuning approach of SVM are presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM, and the improved LS-SVM with wavelet kernel can provide better precision. |
| |
Keywords: | Quantum-inspired evolutionary algorithm (QEA) Parameters tuning Support vector machines (SVM) Least squares support vector machines (LS-SVM) |
本文献已被 维普 万方数据 等数据库收录! |
| 点击此处可从《自然科学进展(英文版)》浏览原始摘要信息 |
| 点击此处可从《自然科学进展(英文版)》下载免费的PDF全文 |