A Classification Method of SVM Based on AFSA

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 δ^2 are initialized; secondly, AFSA is applied to optimize the parameters to gain 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.