A Practical Approach to Model Selection for Support Vector Machines With a Gaussian Kernel

2011 ◽  
Vol 41 (2) ◽  
pp. 330-340 ◽  
Author(s):  
Matthias Varewyck ◽  
Jean-Pierre Martens
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Practical Approach ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Vector Machines ◽  
Selection For

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

scholarly journals Model selection for support vector machines via uniform design

2007 ◽  
Vol 52 (1) ◽  
pp. 335-346 ◽  
Author(s):  
Chien-Ming Huang ◽  
Yuh-Jye Lee ◽  
Dennis K.J. Lin ◽  
Su-Yun Huang
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Uniform Design ◽  
Support Vector ◽  
Vector Machines ◽  
Selection For
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