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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.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.ncbi.nlm.nih.gov/pubmed/12816571
Asymptotic behaviors of support vector machines with Gaussian kernel. Keerthi SS(1), Lin CJ. Author information: (1)Department of Mechanical Engineering, National University of Singapore, Singapore 119260, Republic of Singapore. [email protected] by: 1895
https://www.mitpressjournals.org/doi/abs/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
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.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://scholar.google.com/citations?user=SLMkts8AAAAJ&hl=en
Asymptotic behaviors of support vector machines with Gaussian kernel. SS Keerthi, CJ Lin. ... 2003: Working set selection using second order information for training support vector machines. RE Fan, PH Chen, CJ Lin. Journal of machine learning research 6 (Dec), 1889-1918, 2005. 1830: 2005: Projected gradient methods for nonnegative matrix ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.2053
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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 ...
https://www.sciencedirect.com/science/article/pii/S0957417410011851
Research highlights A genetic algorithm with feature chromosomes (GAFC) is proposed. The asymptotic behaviors of support vector machines (SVM) are fused with GA. The GAFC has not only the search ability of GA, but also has the search ability of feature chromosomes. The GAFC obtained good performances by optimizing feature subset and parameters of SVM simultaneously.Cited by: 150
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