摘要
在实际应用中,支持向量机的性能依赖于参数的选择。针对支持向量机的参数选择问题进行了研究和分析,提出了基于均匀设计的支持向量机参数优化方法。与基于网格搜索、粒子群算法、遗传算法等支持向量机参数优化方法进行了比较与分析,采用多个不同规模的标准的分类数据集进行测试,比较了四种方法的分类正确率和运行时间。仿真实验表明,四种方法都能找到最优参数,使支持向量机的分类正确率接近或超过分类数据集的理论精度,本文方法具有寻参时间短的特点。
In practical applications, the performance of Support Vector Machine （SVM） depends on the selection of parameters. SVM parameter selection problems are studied and analyzed, and a parameter optimization method of SVM based on uniform design is proposed. Our method is compared with the parameter optimization methods of SVM based on grid search, particle swarm optimization, and genetic algorithms. Multiple classification data sets with different sizes are used for testing, so as to compare the classification accuracy and runtime of the four methods. Simulation results show that all the four methods can find the optimal parameters, which make the classification accuracy of SVM approach or exceed the theoretical accuracy of categorical datasets, and our proposed method has the characteristic of finding parameters in short time.
出处
《计算机工程与科学》
CSCD
北大核心
2014年第4期702-706,共5页
Computer Engineering & Science
基金
国家科技部科技支撑计划项目(2013BAJ10B14-5)
国家住建部科研项目(2010FJ3041)
湖南省科技计划资助项目(2012GK3091
2012GK3086)
湖南工业大学自然科学研究资助项目(2011HZX31)
湖南工业大学研究生创新基金资助项目(CX1308)
关键词
支持向量机
参数优化方法
均匀设计
网格搜索
粒子群算法
遗传算法
support vector machine
parameter optimization method
uniform design
grid search
particle swarm optimization
genetic algorithm
