Abstract
Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD.
Original language | English |
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Title of host publication | The 2012 International Joint Conference on Neural Networks (IJCNN) |
Editors | Hussein Abbass |
Place of Publication | New York |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1984-1991 |
Number of pages | 8 |
ISBN (Print) | 9781467314893 |
DOIs | |
Publication status | Published - 2012 |
Event | WCCI 2012 IEEE World Congress on Computational Intelligence - Brisbane, Brisbane, Australia Duration: 10 Jun 2012 → 15 Jun 2012 |
Conference
Conference | WCCI 2012 IEEE World Congress on Computational Intelligence |
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Country/Territory | Australia |
City | Brisbane |
Period | 10/06/12 → 15/06/12 |