Research of Text Categorization Based on Support Vector Machine
Zhou-yu Liao · Jisuanji fangzhen · 2013
Text characterization usually has the characteristics of high dimensional and unbalanced,which causes the probems that traditional classification algorithm accuracy is not high,the performance of text categorization is vulnerable to the influence of kernel function and parameters.In order to improve the accuracy of the text classifier,this article used the support vector machine(SVM) theory to study the text classification technology,and the theory of particle swarm optimization(PSO) algorithm,the classification algorithm was introduced to the SVM to optimize the parameters of the text classifie,we used the accuracy of the classifier as fitness functions,used particles move operation to find the best parameters,and used the SVM algorithm to classify the texts.Compared with the traditional algorithm,the new classifier has higher accuracy.