摘要
由于支持向量机的主要参数的选择能够在很大程度上影响分类性能和效果,并且目前参数优化缺乏理论指导,提出一种粒子群优化算法以优化支持向量机参数的方法.该方法通过引入非线性递减惯性权值和异步线性变化的学习因子策略来改善标准粒子群算法的后期收敛速度慢、易陷入局部最优的缺陷.实验结果表明,相对于标准粒子群算法,本方法在参数优化方面具有良好的鲁棒性、快速收敛和全局搜索能力,具有更高的分类精确度和效率.
Since the selection of the main parameters of the support vector machine can affect the classification performance and effect to a large extent, and the current parameter optimization lacks theoretical guidance, a particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. This method improves the shortcomings of the standard particle swarm optimization algorithm with slow convergence rate and easy to fall into local optimum by introducing nonlinear decreasing inertia weight and asynchronous linear variation learning factor strategy. The experimental results show that compared with the standard particle swarm optimization algorithm, the proposed method has good robustness, fast convergence and global search ability in parameter optimization, and has higher classification accuracy and efficiency.
作者
贺心皓
罗旭
HE Xin-Hao;LUO Xu(School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China)
出处
《计算机系统应用》
2019年第8期241-245,共5页
Computer Systems & Applications
关键词
支持向量机
粒子群优化算法
SVM
参数优化
惯性权值非线性递减
异步变化学习因子
support vector machine
particle swarm optimization algorithm
SVM parameter optimization
inertia weight nonlinear decrement
asynchronous change learning factor
