scholarly journals Selection of time instants and intervals with Support Vector Regression for multivariate functional data

2020 ◽  
Vol 123 ◽  
pp. 105050 ◽  
Author(s):  
Rafael Blanquero ◽  
Emilio Carrizosa ◽  
Asunción Jiménez-Cordero ◽  
Belén Martín-Barragán
Keyword(s):  
Support Vector Regression ◽  
Functional Data ◽  
Support Vector ◽  
Multivariate Functional Data ◽  
Selection Of

Parameter C Optimizing for Robust ε-Support Vector Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2045-2048
Author(s):  
Yuan Lv ◽  
Zhong Gan
Keyword(s):  
Experimental Data ◽  
Support Vector Regression ◽  
System Parameter ◽  
Forecast Accuracy ◽  
Regression Method ◽  
Support Vector ◽  
Penalty Parameter ◽  
Optimal Method ◽  
Good Forecast ◽  
Selection Of

In case of experimental data contaminated with errors and noise, the robust ε-support vector regression has good forecast accuracy and high generalization ability. However, it depends on the selection of system parameter. Firstly, this paper introduces the robust ε-support vector regression method. Secondly, as the experiments prove, the new method achieves high forecast accuracy by virtue of the optimal penalty parameter C. Finally, the optimal method of parameter C is presented in the last section.

Support vector regression for functional data

2018 ◽  
Vol 48 (3) ◽  
pp. 409
Author(s):  
Chen Heng ◽  
Chen Dirong ◽  
Huang Wei
Keyword(s):  
Support Vector Regression ◽  
Functional Data ◽  
Support Vector

Support vector regression for functional data in multivariate calibration problems

2009 ◽  
Vol 642 (1-2) ◽  
pp. 110-116 ◽  
Author(s):  
Noslen Hernández ◽  
Isneri Talavera ◽  
Rolando J. Biscay ◽  
Diana Porro ◽  
Marcia M.C. Ferreira
Keyword(s):  
Support Vector Regression ◽  
Functional Data ◽  
Multivariate Calibration ◽  
Support Vector
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