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Neural Computing and Applications

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24.05.2016 | Original Article

Ellipsoidal support vector data description

verfasst von: Kasemsit Teeyapan, Nipon Theera-Umpon, Sansanee Auephanwiriyakul

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

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Abstract

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.

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Titel: Ellipsoidal support vector data description
verfasst von: Kasemsit Teeyapan
Nipon Theera-Umpon
Sansanee Auephanwiriyakul
Publikationsdatum: 24.05.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2343-3

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