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Can I read Variable-weighted Least-squares Support Vector Machine for Multivariate Spectral Analysis on EtoBox?
Variable-weighted Least-squares Support Vector Machine for Multivariate Spectral Analysis by Hong-Yan Zou; Hai-Long Wu; Hai-Yan Fu; Li-Juan Tang; Lu Xu; Jin-Fang Nie; Ru-Qin Yu is a Chemistry article available to read on EtoBox.
What is Variable-weighted Least-squares Support Vector Machine for Multivariate Spectral Analysis about?
Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.
Who reads Variable-weighted Least-squares Support Vector Machine for Multivariate Spectral Analysis?
It is typically read by researchers, students, and practitioners in Chemistry.
- Author
- Hong-Yan Zou; Hai-Long Wu; Hai-Yan Fu; Li-Juan Tang; Lu Xu; Jin-Fang Nie; Ru-Qin Yu
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0039-9140)
- Published
- 2010
- Language
- EN
- Field
- Chemistry (Physical Sciences)