一种基于AFSA的SVM分类方法(英文)

A classification method of SVM based on AFSA

应用一种全局搜索方法即人工鱼群算法(artificial fish swarm algorithm,AFSA)来优化支持向量基(support vector machines,SVM)的参数,并应用于图像分类。基于分类,初始化惩罚系数C和核函数参数δ2的范围;利用AF-SA来优化SVM的参数,并得到合适的值;最后,把参数优化后的SVM应用于分类。实验结果表明,与C-SVC和交叉验证法相比,其分类结果优于其它两种方法,因此AFSA-SVM方法有更好的准确性和鲁棒性。

In this paper, artificial fish swarm algorithm （AFSA } that is a global search method to optimize the parameters of support vector machines （ SVM ） is applied and modified for image classification. In the classification, firstly, the range of parameters of punishment C and kernel function 62 are initialized ; secondly, AFSA is applied to optimize the parameters to gaiu suitable values; finally, SVM is used for classification, in which the parameters are optimized. By comparing with C-SVC and cross-validate methods, the result excelled another two methods, so the studied algorithm of AFSA-SVM is more accuracy and robust.