Optimizing the tuning parameters of least squares support vector machines regression for NIR spectra-Reference-Cited by-同舟云学术

Optimizing the tuning parameters of least squares support vector machines regression for NIR spectra

Published:2006-05 Issue:5 Volume:20 Page:184-192
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:J. Chemometrics

Author:

Coen T.,Saeys W.,Ramon H.,De Baerdemaeker J.

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Reference13 articles.

1. A twist to partial least squares regression

2. Partial least squares, Beer's law and the net analyte signal: statistical modeling and analysis

3. . Data analysis: calibration of NIR instruments by PLS regression. In Handbook of Near-Infrared Analysis (pp. 681), (eds). Marcel Dekker Inc.: New York, 1992.

4. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes

5. Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds

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