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Least-squares support vector machines for chemometrics: an introduction and evaluation


  • Cogdill, R. & Dardenne, P. (2004). Least-squares support vector machines for chemometrics: an introduction and evaluation. J. Near Infrared Spectrosc. 12: (2), 93-100.
Type Journal Article
Year 2004
Title Least-squares support vector machines for chemometrics: an introduction and evaluation
Journal J. Near Infrared Spectrosc.
Label U15-0043
Recnumber 134
Volume 12
Issue 2
Pages 93-100
Date 2004
Type of article avec comité de lecture
Endnote keywords RA-CRA-W 2003-2004 Di-Chimiométrie
Endnote Keywords chemometrics|support vector machines|artificial neural networks|linear regression|non-parametric regression|radial basis function|
Abstract Support vector machines (SVM) are a relatively new technique for modelling multivariate, non-linear systems, which is rapidly gaining acceptance in many fields. There has been very little application or understanding of SVM methodology in chemometrics. The objectives of this paper are to introduce and explain SVM regression in a manner that will be familiar to the NIR and chemometrics community, and provide some practical comparisons between least-squares SVM regression and more traditional methods of multivariate data analysis. Least squares support vector machines (LS-SVM) were compared to partial least squares (PLS), LOCAL and artificial neural networks (ANN) for regression and classification using four, diverse datasets. LS-SVM was shown to be the most effective algorithm, and required the lowest number of calibration samples to achieve superior predictive performance.
Author address Dardenne Pierre, Quality Department of Agro-food Products, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur, 24, B-5030 Gembloux, dardenne@cra.wallonie.be
Fichier U15-0043-dardenne-2004.pdf
Caption U15-0043-dardenne-2004.pdf
Lien http://www.impublications.com/nir/abstract/J12_0093
Authors Cogdill, R. & Dardenne, P.