A unified model for support vector machine and support vector data description

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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 languageEnglish
Title of host publicationThe 2012 International Joint Conference on Neural Networks (IJCNN)
EditorsHussein Abbass
Place of PublicationNew York
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1984-1991
Number of pages8
ISBN (Print)9781467314893
DOIs
Publication statusPublished - 2012
EventWCCI 2012 IEEE World Congress on Computational Intelligence - Brisbane, Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012

Conference

ConferenceWCCI 2012 IEEE World Congress on Computational Intelligence
CountryAustralia
CityBrisbane
Period10/06/1215/06/12

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Le, T., Tran, D., Ma, W., & Sharma, D. (2012). A unified model for support vector machine and support vector data description. In H. Abbass (Ed.), The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1984-1991). New York: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2012.6252642