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.

引用

页数：5

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