Application of partial least squares support vector machines (PLS-SVM) in spectroscopy quantitative analysis

Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Volumn 1, Issue , 2006, Pages 5228-5232

  Jianfeng, Lv ^a   Liankui, Dai ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Feature extraction; Near infrared spectroscopy (NIRS); Partial least squares (PLS); Quantitative analysis; Support vector machines (SVM)

Indexed keywords

FEATURE EXTRACTION; LEAST SQUARES APPROXIMATIONS; NEAR INFRARED SPECTROSCOPY; SPECTROSCOPIC ANALYSIS;

CALIBRATION MODELS; MODELING TIME; PARTIAL LEAST SQUARES (PLS);

SUPPORT VECTOR MACHINES;

EID: 34047208978     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WCICA.2006.1713389     Document Type: Conference Paper

References (7)

1. Chinese source
2. Litani-Barzilai, L, I. Sela, V. Bulatov, I. Zilberman, I. Schechter. On-line remote prediction of gasoline properties by combined optical methods. Analytica Chimica Acta, 1997, 339: 193-199.
3. Chinese source
4. Chinese source
5. Chinese source
6. Suykens, J. A. K., J. De Brabanter, L. Lukas, J. Vandewalle (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1): 85-105.
7. Chinese source