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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2011

01.02.2011 | Original Article

Adaptive huberized support vector machine and its application to microarray classification

verfasst von: Juntao Li, Yingmin Jia, Wenlin Li

Erschienen in: Neural Computing and Applications | Ausgabe 1/2011

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Abstract

This paper proposes an adaptive huberized support vector machine for simultaneous classification and gene selection. By introducing the data-driven weights, the proposed support vector machine can adaptively identify the important genes in groups, thus encouraging an adaptive grouping effect. Furthermore, the shrinkage biases for the coefficients of important genes are largely reduced. A reasonable correlation between the two regularization parameters is also given, based on which the solution paths are shown to be piecewise linear with respect to the single regularization parameter. Experiment results on leukaemia data set are provided to illustrate the effectiveness of the proposed method.
Vorheriger Artikel The effect of weight fault on associative networks
Nächster Artikel A novel Hash algorithm construction based on chaotic neural network

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Metadaten
Titel
Adaptive huberized support vector machine and its application to microarray classification
verfasst von
Juntao Li
Yingmin Jia
Wenlin Li
Publikationsdatum
01.02.2011
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 1/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-010-0371-y

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