%0 Journal Article %1 verdocnk:2009 %A Debruyne, Michiel %A Serneels, Sven %A Verdonck, Tim %C Department of Mathematics and Computer Science, University of Antwerp, Belgium; LS Services and Consultancy, Edegem, Belgium %D 2009 %I John Wiley & Sons %J Journal of Chemometrics %K SVM classification robust %N 9 %P 479-486 %R 10.1002/cem.1241 %T Robustified least squares support vector classification %U http://dx.doi.org/10.1002/cem.1241 %V 23

@article{verdocnk:2009, added-at = {2009-11-02T23:03:09.000+0100}, address = {Department of Mathematics and Computer Science, University of Antwerp, Belgium; LS Services and Consultancy, Edegem, Belgium}, author = {Debruyne, Michiel and Serneels, Sven and Verdonck, Tim}, biburl = {https://www.bibsonomy.org/bibtex/2357c0cb65f092b70eaa343e7c99d0289/vivion}, description = {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.}, doi = {10.1002/cem.1241}, interhash = {f4d550cda75cd239e93132e80c2632ef}, intrahash = {357c0cb65f092b70eaa343e7c99d0289}, journal = {Journal of Chemometrics}, keywords = {SVM classification robust}, number = 9, pages = {479-486}, publisher = {John Wiley & Sons}, timestamp = {2009-11-02T23:03:09.000+0100}, title = {Robustified least squares support vector classification}, url = {http://dx.doi.org/10.1002/cem.1241}, volume = 23, year = 2009 }