Abstract
One-Class Support Vector Machine employs a grid parameter selection process to discover the best parameters for a given data set. It is assumed that two separate trade-off parameters are assigned to normal and abnormal data samples, respectively. However, this assumption is not always true because data samples have different contributions to the construction of hypersphere or hyperplane decision boundary. In this paper, we introduce a new iterative learning process that is carried out right after the grid parameter selection process to refine the trade-off parameter value for each sample. In this learning process, a weight is assigned to each sample to represent the contribution of that sample and is iteratively refined. Experimental results performed on a number of data sets show a better performance for the proposed approach.
Original language | English |
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Title of host publication | Neural Information Processing |
Subtitle of host publication | 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II |
Editors | Bao-Liang Lu, Liqing Zhang, James Kwok |
Place of Publication | Berlin Germany |
Publisher | Springer-Verlag London Ltd. |
Pages | 529-536 |
Number of pages | 8 |
ISBN (Print) | 9783642249570 |
DOIs | |
Publication status | Published - 2011 |
Externally published | Yes |
Event | International Conference on Neural Information Processing 2011 - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011 Conference number: 18th https://link.springer.com/book/10.1007/978-3-642-24958-7 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 7063 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Neural Information Processing 2011 |
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Abbreviated title | ICONIP 2011 |
Country/Territory | China |
City | Shanghai |
Period | 13/11/11 → 17/11/11 |
Internet address |
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Keywords
- machine learning
- novelty detection
- One class classification
- support vector machine