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Faculty of mathematics, Semnan university, Semnan, Iran
Abstract
The most popular technique for functional data analysis is the functional principal component approach, which is also an important tool for dimension reduction. Support vector regression is branch of machine learning and strong tool for data analysis. In this paper by using the method of functional principal component regression based on the second derivative penalty, ridge and lasso and support vector regression with four kernels (linear, polynomial, sigmoid and radial) in spectro scopic data, the dependent variable on the predictor variables was modeled. According to the obtained results, based on the proposed criteria for evaluating the goodness of fit, support vector regression with linear kernel and error equal to 0.2 has had the most appropriate fit to the data set.
Roohi, A., Jihadi, F. & Roozbeh, M. (2022). Functional principal component regression versus support vector regression for the analysis of spectroscopic data. Andishe_ye Amari, 27(1), 59-72.
MLA
Roohi, A., Jihadi, F., & Roozbeh, M. "Functional principal component regression versus support vector regression for the analysis of spectroscopic data", Andishe_ye Amari, 27, 1, 2022, 59-72.
HARVARD
Roohi A., Jihadi F., Roozbeh M. (2022). 'Functional principal component regression versus support vector regression for the analysis of spectroscopic data', Andishe_ye Amari, 27(1), pp. 59-72.
CHICAGO
A. Roohi, F. Jihadi & M. Roozbeh, "Functional principal component regression versus support vector regression for the analysis of spectroscopic data," Andishe_ye Amari, 27 1 (2022): 59-72,
VANCOUVER
Roohi A., Jihadi F., Roozbeh M. Functional principal component regression versus support vector regression for the analysis of spectroscopic data. Andishe_ye Amari. 2022;27(1):59-72 (In Persian).
