The Application of Improved SVM for Data Analysis in Tourism Economy

被引：2

作者：

Lin Shi-ting ^{[1
]}

Xue Bo ^{[1
]}

机构：

[1] Qinhuangdao Inst Technol, Qinhuangdao 066100, Hebei, Peoples R China

来源：

2014 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA) | 2014年

关键词：

Support Vector Machine; Tourism Economy; Data Analysis;

D O I：

10.1109/ICICTA.2014.186

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this thesis, the main content of statistical learning theory is firstly introduced briefly, based on this, the basic principle and process of e-SVR (one algorithm of Support Vector Machine for Regression, SVR) is presented. Then this method is used to model tourist traffic prediction and predict one series data (Taian monthly tourist quantity data). Two different kernel functions are employed, and the former's performance is evidently better than the latter's. e-SVR's performance is also compared with that of traditional time series analysis method, and the former outperforms the latter.

引用

页码：769 / 772

页数：4