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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2012

01.11.2012 | Original Article

Efficient support vector data descriptions for novelty detection

verfasst von: Xinjun Peng, Dong Xu

Erschienen in: Neural Computing and Applications | Ausgabe 8/2012

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Abstract

Support vector data description (SVDD) is a very attractive kernel method in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors (SVs). In this paper, an efficient SVDD (E-SVDD) is proposed to improve the prediction speed of SVDD. This proposed E-SVDD first finds some crucial feature vectors by the partitioning-entropy-based kernel fuzzy c-means (KFCM) cluster technique and then uses the images of the preimages of these feature vectors to reexpress the center of SVDD. Hence, the decision function of E-SVDD only contains some crucial kernel terms, and the complexity of E-SVDD is linear in the number of the clusters. The experimental results on several benchmark datasets indicate that E-SVDD not only obtains fast prediction speed but also shows better stable generalization than other methods.
Vorheriger Artikel A novel approach to accelerate the convergence speed of a stochastic multi-agent system using recurrent neural nets
Nächster Artikel An LMI approach for exponential synchronization of switched stochastic competitive neural networks with mixed delays

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Fußnoten
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Metadaten
Titel
Efficient support vector data descriptions for novelty detection
verfasst von
Xinjun Peng
Dong Xu
Publikationsdatum
01.11.2012
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 8/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0625-3

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