Active Learning with Nonparallel Support Vector Machine for Binary Classification

被引:3
|
作者
Zhao, Xi [1 ]
Chen, Zhensong [2 ,3 ,4 ]
Shi, Yong [2 ,5 ]
机构
[1] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
[2] Chinese Acad Sci, Key Res Lab Big Data Min & Knowledge Management, Beijing 100081, Peoples R China
[3] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100081, Peoples R China
[4] Univ Chinese Acad Sci, Beijing 100190, Peoples R China
[5] Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA
来源
2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW) | 2014年
关键词
D O I
10.1109/ICDMW.2014.173
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Labeled data, in real world, is quite scarce compared with unlabeled data. Manual annotation is usually expensive and inefficient. Active learning paradigm is used to handle this problem by identifying the most informative instances to annotate. In this paper, we proposed a new active learning algorithm based on nonparallel support vector machine. Numeric experiment shows the effective performance of the proposed method compared with classical active learning based on support vector machine.
引用
收藏
页码:101 / 104
页数:4
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