Publication
Title
Robustified least squares support vector classification
Author
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.
Language
English
Source (journal)
Journal of chemometrics. - Chichester
Publication
Chichester : 2009
ISSN
0886-9383
Volume/pages
23:9(2009), p. 479-486
ISI
000271787000004
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 19.01.2010
Last edited 03.08.2017
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