一种基于PSO的RBF-SVM模型优化新方法

针对使用径向基核函数的支持向量机,采用粒子群优化方法实现模型优化.基于训练集中样本之间的最近平均距离和最远平均距离,给出参数σ的取值空间,从而减小了超参数搜索的范围,并采用对数刻度进一步提高粒子群优化方法的参数搜索效率.与遗传算法和网格法的对比实验表明,所提出的方法收敛速度更快,得出的超参数更优.

A New Approach for Optimizing the Model of RBF-SVM Based on PSO

For the radial basis function (RBF) kernel based support vector machines (SVM), particle swarm optimization (PSO) is employed to carry out the model optimization. The value space of the parameter is presented on the analysis of the mean shortest distance and mean furthest distance among samples of the training set thus the search region is reduced, logarithmic scale is employed to further improve the search efficiency of PSO. Extensive experiments on comparison with genetic algorithm and grid based approaches indicate that the proposed approach converges faster and produces better hyper-parameters.