摘要
支持向量机是基于统计学习理论的结构风险最小化原理基础上提出来的一种学习算法,其在理论上保证了模型的最大泛化能力。针对支持向量机结构参数的选取在没有理论支持,选取又比较困难的情况下,对影响模型分类能力的相关参数进行了研究,提出了一种基于遗传算法和十折交叉检验相结合的遗传支持向量机(GA-SVM)算法,利用遗传算法的全局搜索特性得到支持向量机(SVM)的最优参数值,并用算例表明了此算法有效提高了分类的精度和效率。
The support vector machine （SVM）, which is based on the statistical learning theory and the structural risk minimum principle, guarantees the largest generalization ability of a model. Aiming at the parameters selection of support vector machine still lacks theory support and is very difficult to select. A genetic support vector machine algorithm is proposed based on genetic algorithm and tenfold crossing. The most optimal parameters are obtained by genetic algorithm random search character. Finally, calculation instances show the effectiveness of the optimization algorithm and improved the precision and efficiency of classification effectively.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第19期5016-5018,共3页
Computer Engineering and Design
关键词
支持向量机
遗传算法
参数优化
十折交叉
核函数
support vector machine
genetic algorithm
parameters optimization
tenfold crossing
kernel function
