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Parallel support vector data description

conference contribution
posted on 2013-01-01, 00:00 authored by Phuoc NguyenPhuoc Nguyen, D Tran, X Huang, W Ma
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.

History

Volume

7902

Pagination

280-290

Location

Puerto de la Cruz, Tenerife, Spain

Start date

2013-06-12

End date

2013-06-14

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783642386787

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2013, Springer-Verlag Berlin Heidelberg

Editor/Contributor(s)

Rojas I, Joya G, Gabestany J

Title of proceedings

IWANN 2013 : Proceedings of the 12th International Work-Conference on Artificial Neural Networks 2013

Event

IEEE Computational Intelligence Society. Conference (12th : 2013 : Puerto de la Cruz, Tenerife, Spain)

Issue

Part 1

Publisher

Springer

Place of publication

Berlin, Germany

Series

IEEE Computational Intelligence Society Conference

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