Novelty detection using level set methods
- Submitting institution
-
Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 96989392
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2014.2320293
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 576
- Volume
- 26
- Issue
- 3
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2014
- URL
-
http://dx.doi.org/10.1109/TNNLS.2014.2320293
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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C - Cybersecurity, privacy and human centred computing
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a unique and effective approach to novelty detection. The formulated deformable boundary framework enables direct manipulation of the global boundary of the data distribution, and motivated new locally adaptive boundary evolution algorithms (e.g. https://doi.org/10.1109/IJCNN.2014.6889399 and https://doi.org/10.1109/SMC.2013.515) for solving the challenging problem of decision boundary evolution in machine learning. Li gave invited talks on this work in Tsinghua University and Waikato University.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -