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https://www.csie.ntu.edu.tw/~cjlin/papers/limit.pdf
Manuscript Number: 2621 Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel S. Sathiya Keerthi Department of Mechanical Engineering National University of Singapore Singapore 119260, Republic of Singapore [email protected] Chih-Jen Lin Department of Computer Science and Information Engineering National Taiwan UniversityCited by: 1895
https://www.researchgate.net/publication/10698899_Asymptotic_Behaviors_Of_Support_Vector_Machines
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 ...
https://www.ncbi.nlm.nih.gov/pubmed/12816571
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 sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values.Cited by: 1895
https://www.academia.edu/2834509/Asymptotic_behaviors_of_support_vector_machines_with_Gaussian_kernel
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
https://dl.acm.org/citation.cfm?id=860154
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.Cited by: 1895
https://www.mitpressjournals.org/doi/10.1162/089976603321891855
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.Cited by: 1895
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.880
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 paper analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values.
https://www.semanticscholar.org/paper/Asymptotic-Behaviors-of-Support-Vector-Machines-Keerthi-Lin/b3f45ff1e0a749d6b4fd903dcd844582162469ce
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 . 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 ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.2053
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 . This paper analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values.
http://scholar.google.com/citations?user=SLMkts8AAAAJ&hl=en
A practical guide to support vector classification. CW Hsu, CC Chang, CJ Lin ... Asymptotic behaviors of support vector machines with Gaussian kernel. SS Keerthi, CJ Lin. Neural computation 15 (7), 1667-1689, 2003. 1871: 2003: Working set selection using second order information for training support vector machines. RE Fan, PH Chen, CJ Lin ...
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