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Title
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Robustified least squares support vector classification
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Author
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Abstract
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| Support vector machine (SVM) algorithms are a popular class of techniques to perform classification. However, outliers in the data can result in bad global misclassification percentages. In this paper, we propose a method to identify such outliers in the SVM framework. A specific robust classification algorithm is proposed adjusting the least squares SVM (LS-SVM). This yields better classification performance for heavily tailed data and data containing outliers. |
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Language
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English
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Source (journal)
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Journal of chemometrics. - Chichester
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Publication
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Chichester
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2009
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ISSN
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0886-9383
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DOI
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10.1002/CEM.1241
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Volume/pages
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23
:9
(2009)
, p. 479-486
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ISI
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000271787000004
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Full text (Publisher's DOI)
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