Web Page Classification Based-on A Least Square Support Vector Machine with Latent Semantic Analysis | |
Zhang, Yong; Fan, Bin; Xiao, Long-bin | |
2008 | |
DOI | 10.1109/FSKD.2008.259 |
页码 | 528-532 |
英文摘要 | Chinese web page classification(WPC) has been considered as a hot research area in data mining. In order to effectively classify web pages, we present a web page categorization based on a least square support vector machine(LS-SVM) with latent semantic analysis (LSA). LSA uses Singular Value Decompostion(SVD) to obtain latent semantic structure Of original term-document matrix solving the polysemous and synonymous keywords problem. LS-SVM is an effective method for learning the classification knowledge from massive data, especially on condition of high cost in getting labeled classical examples. We adopt a novel method of web page expression, and make use of summarization algorithm to reduce the noise of web pages. A preliminary experimental comparison is made showing encouraging results. |
会议录 | FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS
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会议录出版者 | IEEE COMPUTER SOC |
会议录出版地 | 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering ; Mathematics |
WOS记录号 | WOS:000264268600105 |
内容类型 | 会议论文 |
源URL | [http://119.78.100.223/handle/2XXMBERH/38001] ![]() |
专题 | 兰州理工大学 |
通讯作者 | Zhang, Yong |
作者单位 | Lanzhou Univ Tech, Sch Comp & Commun, Lanzhou 730050, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Yong,Fan, Bin,Xiao, Long-bin. Web Page Classification Based-on A Least Square Support Vector Machine with Latent Semantic Analysis[C]. 见:. |
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