基于改进SVM的互联网用户分类

Internet User Classification Based on Improved SVM

由于传统模型大量约束样本,导致其学习能力下降,因此设计一个基于改进支持向量机(Support Vector Machine,SVM)的互联网用户分类模型.该模型通过构造样本数据,模拟互联网用户的浏览轨迹;根据用户偏好,制定全新的用户分类策略;基于改进支持向量机,实现对互联网用户的分类.性能测试:3次实验下,此次设计的模型分类准确率平均值为98.56%,超出了预设的期望值,具备分类能力.对比测试:与两组传统用户分类模型相比,此次设计的模型,面对不断增加的样本数据,同样能保持高水平的学习能力.

The learning ability of traditional models is reduced by copious constrained samples,so an Internet user classification model based on improved Support Vector Machine(SVM)is designed,which simulates the browsing trajectories of Internet users by constructing sample data.A brand-new user classification strategy according to user preferences is formulated.Then,Internet users are classified based on improved SVM.According to the three performance tests,the model has satisfying classification ability because its average accuracy is 98.56%,higher than the expected value.Seen from the comparative tests with two traditional user classification models,this model can maintain a high level of learning ability in the face of increasing sample data.