- 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 |
|---|---|
| Année | 2004 |
| Titre | Least-squares support vector machines for chemometrics: an introduction and evaluation |
| Contenu | 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. |
| Fichier | |
| Journal | J. Near Infrared Spectrosc. |
| 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| |
| 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 |
| Caption | U15-0043-dardenne-2004.pdf |
| Auteurs | Cogdill, R. & Dardenne, P. |