Relevance vector machines for multivariate calibration purposes

Journal of Chemometrics

Volumn 22, Issue 11-12, 2008, Pages 686-694

  Hernández, Noslen ^a   Talavera, Isneri ^a   Dago, Angel ^b   Biscay, Rolando J ^c   Castro Ferreira, Marcia M ^d   Porro, Diana ^a  

^a ADVANCED TECHNOLOGIES APPLICATION CENTER   (Cuba)

^b Research Petroleum Center   (Cuba)

^c INSTITUTO DE CIBERNÉTICA   (Cuba)

^d UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Bayesian learning; Kernel methods; Multivariate calibration; Relevance vector machines

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; LEAST SQUARES APPROXIMATIONS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; VECTORS;

BAYESIAN LEARNING; CHEMOMETRICES; KERNEL-METHODS; LEAST SQUARE SUPPORT VECTOR MACHINES; MULTIVARIATE CALIBRATION; REGRESSION METHOD; RELEVANCE VECTOR MACHINE; SUPPORT VECTOR MACHINE METHOD; SUPPORT VECTOR REGRESSIONS; SVM-BASED METHODS;

CALIBRATION;

EID: 57149086674     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1168     Document Type: Conference Paper

References (25)

1. Swierenga H. Robust multivariate calibration models in vibrational spectroscopic applications. Ph.D. Thesis, University of Nijmegen, 2000.
2. Hageman JA, Streppel M, Wehrens R, Bydens LMC. Wave length selection with Tabu search. J. Chemom. 2003; 17: 427-437.
3. Estienne F, Massart DL. Multivariate calibration with Raman data using fast principal components and partial least square method. Anal. Chim. Acta 2001; 450: 123-129.
4. Bishop CM. Neural Networks for Pattern Recognition. Clarendon Press: Oxford, 1995.
5. Belousov AI, Versakov SA, Von Frese J. A flexible classification approach with optimal generalization performance: support vector machines. Chemon. Intell. Lab. Syst. 2002; 64: 15-25.
6. Belousov AI, Versakov SA, Von Frese J. Application aspects of supports vector machines. J. Chemom. 2002; 16: 482-489.
7. Thissen U, van Brakel R, de Weijer AP, Melssen WJ, Bydens LMC. Using support vector machines for time series prediction. Chemon. Intell. Lab. Syst. 2003; 69: 35-49.
8. Thissen U, Pepers M, Üstun B, Melssen WJ, Bydens LMC. Comparing support vector machines to PLS for spectral regression applications. Chemon. Intell. Lab. Syst. 2004; 73: 169-179.
9. Thissen U, Üstun B, Melssen WJ, Bydens LMC. Multivariate calibration with least-squares support vector machines. Anal. Chem. 2004, 76: 3099-3105.
10. Suykens JAK, Van Gestel T, De Branmaster J, De Moor B, Vandewalle J. Least Square Support Vector Machine. World Scientific: Singapore, 2002.
11. Tipping ME. The relevance vector machines. In Advances in Neural Information Processing Systems 12, Solla SA, Leen TK, Muller K-R (eds). MIT Press: Cambridge, MA, 2000; 652-658.
12. Mackay DJC. Bayesian interpolation. Neural Comput. 1992; 4(3): 415-447.
13. Berger JO. Statistical Decision Theory and Bayesian Analysis (2nd edn). Springer: New York, 1985.
14. Faul AC, Tipping ME. Analysis of sparse Bayesian learning. In Advances in Neural Information Processing Systems 14, Dietterich TG, Becker S, Ghahramani Z (eds). MIT Press: Cambridge, MA, 2002; 383-389.
15. Tipping ME. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 2001; 1: 211-244.
16. Ẅulfert F, Kok W. Th, Smilde AK. Anal. Chem. 1998; 70: 1761-1767.
17. Tecator dataset. Available at Statlib: http://lib.stat.cmu.edu/datasets/ tecator
18. Dyrby M, Engelsen SB, Nrgaard L, Bruhn M, Lundsberg-Nielsen L. Chemometric quantitation of the active substance in a pharmaceutical tablet using near infrared (NIR) transmittance and NIR FT Raman spectra. Appl. Spectrosc. 2002; 56(5): 579-585.
19. Tablet Dataset. Available at http://www.models.kvl.dk.research/data/ Tablets/
20. Matlab Toolbox for Kernel Methods: The Spider. Available at http://www.kyb.tuebingen.mpg.de/bs/people/spider/
21. LS-SVMlab: a Matlab/C toolbox for least squares support vector machines; 2002. Available at http://www.esat.kuleuven.ac.be/sista/lssvmlab/
22. Tipping ME. "SparseBayes" a Matlab implementation of sparse Bayesian learning, 2001. Available at http://research.microsoft.com/mlp/rvm/
23. Rossi F, Lendasse A, Francois D, Wertz V, Verleysen M. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemometr. Intell. Lab. Syst. 2006; 80(2): 215-226
24. Chen T, Morris J, Martin E. Gaussian process regression for multivariate spectroscopic calibration. Chemon. Intell. Lab. Syst. 2007; 87: 59-71.
25. Tipping M, Faul A. Fast marginal likelihood maximization for sparse Bayesian models. In Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics, 2003.