Parallel Support Vector Data Description

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3 Citations (Scopus)
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This paper proposes an extension of Support Vector Data Description (SVDD) to provide a better data description. The extension is called Distant SVDD (DSVDD) that determines a smallest hypersphere enclosing all normal (positive) samples as seen in SVDD. In addition, DSVDD maximises the distance from centre of that hypersphere to the origin. When some abnormal (negative) samples are introduced, the DSVDD is extended to Parallel SVDD that also determines a smallest hypersphere for normal samples and at the same time determines a smallest hyperphere for abnormal samples and maximises the distance between centres of these two hyperspheres. Experimental results for classification show that the proposed extensions provide higher accuracy than the original SVDD.
Original languageEnglish
Title of host publicationInternational Work-Conference on Artificial Neural Networks:Advances in Computational Intelligence (IWANN 2013)
Subtitle of host publicationLecture Notes in Computer Science
EditorsIgnacio Rojas, Gonzalo Joya, Joan Gabestany
Place of PublicationBerlin
Number of pages11
ISBN (Electronic)9783642386794
ISBN (Print)9783642386787
Publication statusPublished - 2013
Event12th International Work-Conference on Artificial Neural Networks, IWANN 2013 - Tenerife, Tenerife, Spain
Duration: 12 Jun 201314 Jun 2013


Conference12th International Work-Conference on Artificial Neural Networks, IWANN 2013
Abbreviated titleIWANN 2013


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