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Pattern Analysis and Applications

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01.08.2012 | Theoretical Advances

An extension to fuzzy support vector data description (FSVDD*)

verfasst von: Y. Forghani, H. Sadoghi Yazdi, S. Effati

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2012

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Abstract

The well-known support vector data description (SVDD) is based on precise description of precise data. When we know the features of training samples precisely and we are uncertain about their class labels, the fuzzy SVDD can be used to obtain the data description. But if the features of training samples are fuzzy numbers, the fuzzy SVDD cannot be utilized. In this paper, we extend the fuzzy SVDD for the description of such training samples and then apply our proposed method, called FSVDD*, to real data. The experimental results show the ability of the proposed method in Taiwanese tea evaluation.

Vorheriger Artikel Maximized subspace model for hyperspectral anomaly detection

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Titel: An extension to fuzzy support vector data description (FSVDD*)
verfasst von: Y. Forghani
H. Sadoghi Yazdi
S. Effati
Publikationsdatum: 01.08.2012
Verlag: Springer-Verlag
Erschienen in: Pattern Analysis and Applications / Ausgabe 3/2012
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-011-0208-z

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