Searching for Asymptotic Behaviour Of Support Vector Machines With Gaussian Kernel Bibtex information? Find all needed info by using official links provided below.
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://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.
https://www.csie.ntu.edu.tw/~cjlin/papers/limit.pdf
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.Cited by: 1895
https://www.researchgate.net/publication/10698899_Asymptotic_Behaviors_Of_Support_Vector_Machines
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 the kernel width sigma.
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.
https://link.springer.com/article/10.1007/s10463-019-00727-1
Jul 15, 2019 · In particular, we investigate asymptotic properties of the BC-SVM having the Gaussian kernel and compare them with the ones having the linear kernel. We show that the performance of the BC-SVM is influenced by the scale parameter involved in the Gaussian kernel.Author: Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
https://www.researchgate.net/publication/2238051_Asymptotic_Behaviour_of_Support_Vector_Machines
Least squares support vector machines (LS-SVM) based on a group of different kernel functions (Linear-Polynomial-Radial Basis Function- Exponential Radial Basis Function) for modeling nonlinear ...
https://www.sciencedirect.com/science/article/pii/S0047259X11002090
The goal is to estimate a function f:X→R which minimizes this risk. The estimates obtained from the method of support vector machines are elements of so-called reproducing kernel Hilbert spaces (RKHS) H. A RKHS H is a certain Hilbert space of functions f:X→R which is generated by a kernel k…Cited by: 26
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