design of text categorization system based on svm

ISCAS OpenIR

	design of text categorization system based on svm
	Liu Zhenyan; Wang Weiping; Wang Yong
	2012
会议名称	2012 2nd International Conference on Materials Science and Information Technology, MSIT 2012
会议录名称	Advanced Materials Research
页码	1191-1195
会议日期	August 24, 2012 - August 26, 2012
会议地点	Xi'an, Shaan, China
收录类别	EI
ISSN	1022-6680
ISBN	9783037854389
部门归属	(1) Institute of Computing Technology Chinese Academy of Sciences China; (2) Graduate University Chinese Academy of Sciences China; (3) School of Software Beijing Institute of Technology China
摘要	This paper introduces the design of a text categorization system based on Support Vector Machine (SVM). It analyzes the high dimensional characteristic of text data, the reason why SVM is suitable for text categorization. According to system data flow this system is constructed. This system consists of three subsystems which are text representation, classifier training and text classification. The core of this system is the classifier training, but text representation directly influences the currency of classifier and the performance of the system. Text feature vector space can be built by different kinds of feature selection and feature extraction methods. No research can indicate which one is the best method, so many feature selection and feature extraction methods are all developed in this system. For a specific classification task every feature selection method and every feature extraction method will be tested, and then a set of the best methods will be adopted. © (2012) Trans Tech Publications, Switzerland.; This paper introduces the design of a text categorization system based on Support Vector Machine (SVM). It analyzes the high dimensional characteristic of text data, the reason why SVM is suitable for text categorization. According to system data flow this system is constructed. This system consists of three subsystems which are text representation, classifier training and text classification. The core of this system is the classifier training, but text representation directly influences the currency of classifier and the performance of the system. Text feature vector space can be built by different kinds of feature selection and feature extraction methods. No research can indicate which one is the best method, so many feature selection and feature extraction methods are all developed in this system. For a specific classification task every feature selection method and every feature extraction method will be tested, and then a set of the best methods will be adopted. © (2012) Trans Tech Publications, Switzerland.
关键词	Classification (Of Information) Feature Extraction Image Retrieval Information Technology Materials Science Text Processing
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15957
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Liu Zhenyan,Wang Weiping,Wang Yong. design of text categorization system based on svm[C],2012:1191-1195.