Webpages Classification Based on Deep Belief Network Using Images and Text Information

被引：0
|
作者：
Hu, Ruiguang [1 ]
Gao, Shibo [1 ]
Yang, Libo [1 ]
机构：
[1] Beijing Aerosp Automat Control Inst, Natl Key Lab Sci & Technol Aerosp Intelligent Con, Beijing 100854, Peoples R China
来源：
2018 IEEE CSAA GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC) | 2018年
关键词：
WEB PAGES;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, Deep Belief Network(DBN) is used for drug-related webpages classification. DEVIL parsing is used to extract image-label text and body text, FOCARSS method is used to choose effective images. text representation is generated by BOW model, images representation is generated by BOF model. We concatenate images and text representation to generate final representation. It is shown that DBN's classification accuracy is higher than BPNN's classification accuracy, and better than that of single-modal information.
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页数：4
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