Asymptotic Behaviour Of Support Vector Machines With Gaussian Kernel Bibtex

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Asymptotic Behaviors of Support Vector Machines with ...

    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.

Asymptotic behaviors of support vector machines with ...

    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

Asymptotic Behaviors of Support Vector Machines with ...

    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.

Manuscript Number: 2621 Asymptotic Behaviors of Support ...

    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

Asymptotic Behaviors Of Support Vector Machines ...

    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.

Asymptotic Behaviors Of Support Vector Machines . . . . (2003)

    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.

Bias-corrected support vector machine with Gaussian kernel ...

    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

(PDF) Asymptotic Behaviour of Support Vector Machines

    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 ...

Asymptotic normality of support vector machine variants ...

    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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