Optimal Selection of Support Vector Regression Parameters and Molecular Descriptors for Retention Indices Prediction

2010 ◽  
pp. 83-90 ◽  
Author(s):  
Jun Zhang ◽  
Bing Wang ◽  
Xiang Zhang
Keyword(s):  
Support Vector Regression ◽  
Molecular Descriptors ◽  
Retention Indices ◽  
Support Vector ◽  
Optimal Selection ◽  
Regression Parameters ◽  
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.

Optimisation of Support Vector Regression Parameters

NIR news ◽  
2006 ◽  
Vol 17 (4) ◽  
pp. 12-13
Author(s):  
Bülent Üstün ◽  
Willem Melssen ◽  
Lutgarde Buydens
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Regression Parameters

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

scholarly journals Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hailun Wang ◽  
Daxing Xu
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Unscented Kalman Filter ◽  
Selection Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Regression Parameters ◽  
Cubature Kalman Filter ◽  
Adaptive Fusion

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

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