摘要
支持向量机中惩罚参数和核参数的取值对其性能影响较大,但在最优参数选取上一直缺少理论指导。依据分裂选择的思想对遗传算法的选择操作做改进,以保持种群的多样性,同时利用支持向量计数法构造适应度函数,用改进后的遗传算法对支持向量机进行参数优化,不仅在一定程度上避免过早陷入局部最优,还能提高计算效率。实验表明,用改进的遗传算法优化支持向量机的参数耗时更短,并且得到的支持向量机有更好的分类效果。
The selection of support vector machine＆#39;s punished parameter and kernel parameter will affect its performance greatly and there are no theory for guiding the selection of its optimal parameters. Improves selection operation of genetic algorithm to maintain the diversity of the population based on the idea of disruptive selection. Applies the improved genetic algorithm for optimizing SVM parameters, and uses the support vectors counting method to construct the fitness function, which avoid falling into local optimum in some extent and reduce the computation greatly. Experiments show that the improved algorithm can optimize the parameters of SVM with shorten running time, and the support vector machine will get better classification performance.
出处
《现代计算机》
2014年第6期25-29,34,共6页
Modern Computer
基金
上海市科委科技创新项目(No.12595810200)
关键词
支持向量机
参数优化
遗传算法
分裂选择
SVM
Parameter Optimization
Genetic Algorithm
Disruptive Selection
