Automated text categorization using support vector machine

被引：0
|
作者：
Kwok, JTY [1 ]
机构：
[1] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong
来源：
ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3 | 1998年
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we study the use of support vector machine in text categorization. Unlike other machine learning techniques, it allows easy incorporation of new documents into an existing trained system. Moreover, dimension reduction, which is usually imperative, now becomes optional. Thus, SVM adapts efficiently in dynamic environments that require frequent additions to the document collection. Empirical results on the Reuters-22173 collection are also discussed.
引用
收藏
页码：347 / 351
页数：5
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