重庆理工大学学报（自然科学版）2017，Vol.31Issue(7)：140-144,5.DOI:10.3969/j.issn.1674-8425(z).2017.07.022
基于PCA和PSO-SVM的手写数字识别应用研究
Application Research of Handwritten Numeral Recognition Based on PCA and PSO-SVM
摘要
Abstract
In this paper,a new method of handwritten numeral recognition based on principal component analysis (PCA) and particle swarm optimization (PSO-SVM) is proposed for the problem of low accuracy of handwritten digit recognition.Firstly,the dimension of the input data is reduced by PCA,then the dimension reduction data is used as the input of SVM,and the kernel function parameter g and the penalty factor c in SVM are optimized by PSO to improve the classification accuracy.The experimental results show that SVM and GA-SVM,with the traditional grid search algorithm,convolutional neural network (CNN) compared with the classification method of PSO-SVM method and it has higher recognition accuracy rate and the operation efficiency is the highest,reached 98.2％,and the performance is better than other types of classification algorithms.关键词
主成分分析/粒子群算法/支持向量机/手写数字识别Key words
principal component analysis/particle swarm algorithm/support vector machine/handwritten numeral recognition分类
信息技术与安全科学引用本文复制引用
张校非,白艳萍,郝岩..基于PCA和PSO-SVM的手写数字识别应用研究[J].重庆理工大学学报（自然科学版）,2017,31(7):140-144,5.基金项目
国家自然科学基金资助项目(61275120) （61275120）
山西省回国留学人员科研资助项目(2016-088) （2016-088）
