A dynamic model selection strategy for support vector machine classifiers

2012 ◽  
Vol 12 (8) ◽  
pp. 2550-2565 ◽  
Author(s):  
Marcelo N. Kapp ◽  
Robert Sabourin ◽  
Patrick Maupin
Keyword(s):  
Support Vector Machine ◽  
Model Selection ◽  
Dynamic Model ◽  
Selection Strategy ◽  
Support Vector

Sensor Dynamic Modeling Based on LS-SVM and NGA

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Support Vector ◽  
Local Minima ◽  
Highly Nonlinear ◽  
Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

scholarly journals A support vector machine for model selection in demand forecasting applications

2018 ◽  
Vol 121 ◽  
pp. 1-7 ◽  
Author(s):  
Marco A. Villegas ◽  
Diego J. Pedregal ◽  
Juan R. Trapero
Keyword(s):  
Support Vector Machine ◽  
Model Selection ◽  
Demand Forecasting ◽  
Support Vector

scholarly journals Multi-factor Stock Selection Model Based on Kernel Support Vector Machine

2018 ◽  
Vol 10 (5) ◽  
pp. 9 ◽  
Author(s):  
Ru Zhang ◽  
Zi-ang Lin ◽  
Shaozhen Chen ◽  
Zhixuan Lin ◽  
Xingwei Liang
Keyword(s):  
Support Vector Machine ◽  
Predictive Power ◽  
Factor Model ◽  
Selection Strategy ◽  
Support Vector ◽  
Stock Selection ◽  
Financial Investment ◽  
Model Based ◽  
Kernel Support Vector Machine ◽  
Selection Of

In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

Sign in / Sign up

Export Citation Format

Share Document