Research on Data Classification Method of Optimized Support Vector Machine Based on Gray Wolf Algorithm

The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability learning method. Among them, the kernel function its parameters have great impact accuracy. In order to find optimal improve accuracy of SVM, this paper proposes multi-classification gray wolf algorithm optimized SVM(GWO-SVM). paper, iris set is test performance GWO-SVM, result compared with those genetic (GA), particle swarm optimization (PSO) original SVM model. results show that GWO-SVM model higher recognition than other three models, shortest running time, which obvious advantages can effectively SVM. This practical significance image classification, text fault detection.