Title: Robustified least squares support vector classification
Authors: Debruyne, Michiel ×
Serneels, Sven
Verdonck, Tim #
Issue Date: Sep-2009
Publisher: Wiley
Series Title: Journal of Chemometrics vol:23 issue:9-10 pages:479-486
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. Copyright (C) 2009 John Wiley & Sons, Ltd.
ISSN: 0886-9383
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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