Web Page Classification Based-on A Least Square Support Vector Machine with Latent Semantic Analysis

doi:10.1109/FSKD.2008.259

CORC > 兰州理工大学 > 兰州理工大学

	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
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:.