Please use this identifier to cite or link to this item: https://doi.org/10.1162/089976603321891855
Title: Asymptotic behaviors of support vector machines with gaussian kernel
Authors: Keerthi, S.S. 
Lin, C.-J.
Issue Date: Jul-2003
Source: Keerthi, S.S., Lin, C.-J. (2003-07). Asymptotic behaviors of support vector machines with gaussian kernel. Neural Computation 15 (7) : 1667-1689. ScholarBank@NUS Repository. https://doi.org/10.1162/089976603321891855
Abstract: Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyperparameters 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.
Source Title: Neural Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/59589
ISSN: 08997667
DOI: 10.1162/089976603321891855
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