西安工程大学学报2012，Vol.26Issue(6)：815-819,5.
一种基于PSO的混合核支持向量机算法
Support vector machine algorithm of hybrid kernel based on PSO
摘要
Abstract
As a new machine learning method, support vector machine algorithm has many obvious advantages in solving the problem of small samples. However, it is important to select an optimal kernel function and parameters in order to enhance the performance of support vector machine algorithm. In this paper, the support vector machine algorithm of hybrid kernel based on PSO is proposed by the mixed kernel function method combined with global kernel function and local kernel function. After Matlab simulation experiment, the results show that the improved algorithm is superior to standard SVM in respect of classification accuracy, learning and generalization ability.关键词
支持向量机/全局核函数/局部核函数/混合核函数/粒子群优化算法Key words
support vector machine/ global kernel function/ local kernel function/ hybrid kernel function/ particle swarm optimization分类
数理科学引用本文复制引用
谌璐,贺兴时,王芳妮,刘平丽..一种基于PSO的混合核支持向量机算法[J].西安工程大学学报,2012,26(6):815-819,5.基金项目
陕西省教育厅专项基金项目(2010JK136) （2010JK136）
