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Fast one-class support vector machine for novelty detection

Le, Trung, Phung, Dinh, Nguyen, Khanh and Venkatesh, Svetha 2015, Fast one-class support vector machine for novelty detection. In Cao, Tru, Lim, Ee-Peng, Zhou, Zhi-Hua, Ho, Tu-Bao, Cheung, David and Motoda, Hiroshi (ed), Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Springer, Berlin, Germany, pp.189-200.

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Title Fast one-class support vector machine for novelty detection
Author(s) Le, TrungORCID iD for Le, Trung orcid.org/0000-0002-7070-8093
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Nguyen, Khanh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Title of book Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I
Editor(s) Cao, Tru
Lim, Ee-Peng
Zhou, Zhi-Hua
Ho, Tu-Bao
Cheung, David
Motoda, Hiroshi
Publication date 2015
Series Lecture notes in computer science; v.9078
Chapter number 15
Total chapters 59
Start page 189
End page 200
Total pages 12
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) one-class support vector machine
novelty detection
large-scale dataset
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
SVM
Summary Novelty detection arises as an important learning task in several applications. Kernel-based approach to novelty detection has been widely used due to its theoretical rigor and elegance of geometric interpretation. However, computational complexity is a major obstacle in this approach. In this paper, leveraging on the cutting-plane framework with the well-known One-Class Support Vector Machine, we present a new solution that can scale up seamlessly with data. The first solution is exact and linear when viewed through the cutting-plane; the second employed a sampling strategy that remarkably has a constant computational complexity defined relatively to the probability of approximation accuracy. Several datasets are benchmarked to demonstrate the credibility of our framework.
ISBN 9783319180328
ISSN 0302-9743
1611-3349
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076882

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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