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Large margin DragPushing strategy for centroid text categorization
Tan, Songbo
2007-07-01
发表期刊EXPERT SYSTEMS WITH APPLICATIONS
ISSN0957-4174
卷号33期号:1页码:215-220
摘要Among all conventional methods for text categorization, centroid classifier is a simple and efficient method. However it often suffers from inductive bias (or model misfit) incurred by its assumption. DragPushing is a very simple and yet efficient method to address this so-called inductive bias problem. However, DragPushing employs only one criterion, i.e., training-set error, as its objective function that cannot guarantee the generalization capability. In this paper, we propose a generalized DragPushing strategy for centroid classifier, which we called as "Large Margin DragPushing" (LMDP). The experiments conducted on three benchmark evaluation collections show that LMDP achieved about one percent improvement over the performance of DragPushing and delivered top performance nearly as well as state-of-the-art SVM without incurring significant computational costs. (c) 2006 Published by Elsevier Ltd.
关键词text classification information retrieval machine learning
DOI10.1016/j.eswa.2006.04.008
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号WOS:000244110600021
出版者PERGAMON-ELSEVIER SCIENCE LTD
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/10993
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tan, Songbo
作者单位1.Chinese Acad Sci, Inst Comp Technol, Software Dept, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Grad Sch, Beijing 100080, Peoples R China
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Tan, Songbo. Large margin DragPushing strategy for centroid text categorization[J]. EXPERT SYSTEMS WITH APPLICATIONS,2007,33(1):215-220.
APA Tan, Songbo.(2007).Large margin DragPushing strategy for centroid text categorization.EXPERT SYSTEMS WITH APPLICATIONS,33(1),215-220.
MLA Tan, Songbo."Large margin DragPushing strategy for centroid text categorization".EXPERT SYSTEMS WITH APPLICATIONS 33.1(2007):215-220.
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