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Probabilistic novelty detection with support vector machines

Abstract:

Novelty detection, or one-class classification, is of particular use in the analysis of high-integrity systems, in which examples of failure are rare in comparison with the number of examples of stable behaviour, such that a conventional multi-class classification approach cannot be taken. Support Vector Machines (SVMs) are a popular means of performing novelty detection, and it is conventional practice to use a train-validate-test approach, often involving cross-validation, to train the one-...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/TR.2014.2315911

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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Sub department:
Centre for Statistics in Medicine
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Centre of Excellence in Personalized Healthcare More from this funder
Royal Academy of Engineering Research Fellowship More from this funder
Overseas Research Students Award Scheme More from this funder
National Institute for Health Research More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Transactions on Reliability Journal website
Volume:
63
Issue:
2
Pages:
455-467
Publication date:
2014-06-01
DOI:
EISSN:
1558-1721
ISSN:
0018-9529
Source identifiers:
471819
Keywords:
Pubs id:
pubs:471819
UUID:
uuid:3cf37979-a4ac-4b47-8ad1-501f63acc8a0
Local pid:
pubs:471819
Deposit date:
2016-01-18

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