Comparing support vector machines to PLS for spectral regression applications

By U. Thissen, Mlh Michel Pepers, B. Üstün, Willem J. Melssen, Lutgarde M. C. Buydens. 2004

370 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics

View Paper: https://www.semanticscholar.org/paper/2888cec756723970debfa6add3acf901b31ed5d1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Martin Schlerf
2. W. Verhoef
3. Christoffer Axelsson
4. Anas Miftah Fauzi
5. Andrew K. Skidmore
6. W. P. Filho
7. R. S. Dalmolin
8. Sabine Grunwald

In Biomedical Engineering

1. Andrew K. Skidmore
2. Christoffer Axelsson
3. Martin Schlerf
4. W. Verhoef
5. Anas Miftah Fauzi
6. Pijush Samui
7. J. C. L. Alves
8. C. B. Henriques

In Biophysics

1. Willem J. Melssen
2. Lutgarde M. C. Buydens
3. J. C. L. Alves
4. C. B. Henriques
5. R. Poppi
6. B. Üstün

Citations in the corpus

• Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression (2013, methodology): “It uses the principle of structural risk minimization to simultaneously optimize performance and generalization and is often able to find non-linear and unique solutions (Thissen et al. 2004b).”
• Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs (2012, background): “With regards to the selection of ε, if ε is too large, too few support vectors are selected, which leads to a decrease of the final prediction performance (Thissen et al., 2004).”
• Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy (2017, result): “The SVM acceptance in soil properties estimation has increased in recent years (Araújo et al., 2014; Ramirez-Lopez et al., 2013; Terra et al., 2015) and has generated more accurate calibration results than PLSR in some studies (Thissen et al., 2004; Viscarra Rossel et al., 2010).”
• Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system (2012, result): “Recently, applications of support vector machine algorithms have demonstrated a sensitive improvement in results compared to that obtained with PLS [19,20], especially for data with high nonlinear relationships or complexities.”
• Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel (2006, methodology): “The procedure to construct the simulated data and a brief description of the used real-world benchmark data sets which were thoroughly studied [20–23] are described below.”