Advances in Artificial Intelligence and Security | 2021
Research on SVM Parameter Optimization Mechanism Based on Particle Swarm Optimization
Abstract
As an common and effective pattern recognition method, support vector machine is used in many scenarios. The parameter selection of support vector machine directly affects its performance, determines its classification accuracy and generalization ability. This paper optimizes the parameter optimization mechanism of support vector machine (SVM) based on particle swarm optimization (PSO), adjusts the parameters of particle swarm optimization, and makes it have stronger global search ability. In this paper, support vector machine is firstly introduced, then the parameter optimization mechanism of support vector machine is elaborated, and the improved parameter selection model based on particle swarm optimization algorithm is proposed, then carries on the simulation experiment to the improved model on the open data set. Experimental results show that the improved model has higher classification accuracy. At last, the shortcomings of the study and other possible acting contents and optimization directions are described, and the focus and direction of the future research work are given.