摘要
支持向量机算法作为一种新的机器学习方法,在处理小样本分类问题上具有明显优势,但核函数和参数的选取直接影响支持向量机算法的性能.针对该问题,文中通过组合全局核函数和局部核函数的混合核函数方法,建立了基于粒子群算法的混合核支持向量机算法,并将其用于ORL人脸数据库的人脸识别测试.结果表明,该改进算法较标准的支持向量机算法具有更高的识别率.
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 based on hybrid kernel and PSO is proposed by the mixed kernel function method combined with global kernel function and local kernel function,and after the classification test on the ORL face database,the results show that the improved algorithm is superior to standard SVM on recognition accuracy.
出处
《纺织高校基础科学学报》
CAS
2015年第1期108-115,共8页
Basic Sciences Journal of Textile Universities
基金
陕西省软科学基金资助项目(2012KRM58)
陕西省教育厅自然科学基金资助项目(12JK0744)
宝鸡文理学院校级重点项目(2K14049)
关键词
支持向量机
混合核函数
粒子群优化算法
人脸识别
support vector machine
hybrid kernel function
particle swarm optimized algorithm
face recognition
