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dc.contributor.authorKasemsit Teeyapanen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.description.abstract© 2016, The Natural Computing Applications Forum. This paper presents a data domain description formed by the minimum volume covering ellipsoid around a dataset, called “ellipsoidal support vector data description (eSVDD).” The method is analogous to support vector data description (SVDD), but instead, with an ellipsoidal domain description allowing tighter space around the data. In eSVDD, a hyperellipsoid extends its ability to describe more complex data patterns by kernel methods. This is explicitly achieved by defining an “empirical feature map” to project the images of given samples to a higher-dimensional space. We compare the performance of the kernelized ellipsoid in one-class classification with SVDD using standard datasets.en_US
dc.subjectComputer Scienceen_US
dc.titleEllipsoidal support vector data descriptionen_US
article.title.sourcetitleNeural Computing and Applicationsen_US
article.volume28en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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