Title
Robustified least squares support vector classification Robustified least squares support vector classification
Author
Faculty/Department
Faculty of Sciences. Chemistry
Publication type
article
Publication
Chichester ,
Subject
Chemistry
Computer. Automation
Source (journal)
Journal of chemometrics. - Chichester
Volume/pages
23(2009) :9 , p. 479-486
ISSN
0886-9383
ISI
000271787000004
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
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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