Fuzzy Entropy Semi-supervised Support Vector Data Description

被引：0
|
作者：
Le, Trung [1 ]
Tran, Dat [2 ]
Tran, Tien [1 ]
Nguyen, Khanh [1 ]
Ma, Wanli [2 ]
机构：
[1] HCMc Univ Pedag, Fac Informat Technol, Hochiminh City, Vietnam
[2] Univ Canberra, Fac Educ Sci Technol & Math, Canberra, ACT, Australia
来源：
2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2013年
关键词：
MACHINES;
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：
Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.
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页数：5
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