Kernel Selection for the Support Vector Machine

Rameswar DEBNATH; Haruhisa TAKAHASHI

Kernel Selection for the Support Vector Machine

Rameswar DEBNATH, Haruhisa TAKAHASHI

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The choice of kernel is an important issue in the support vector machine algorithm, and the performance of it largely depends on the kernel. Up to now, no general rule is available as to which kernel should be used. In this paper we investigate two kernels: Gaussian RBF kernel and polynomial kernel. So far Gaussian RBF kernel is the best choice for practical applications. This paper shows that the polynomial kernel in the normalized feature space behaves better or as good as Gaussian RBF kernel. The polynomial kernel in the normalized feature space is the best alternative to Gaussian RBF kernel.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.12 pp.2903-2904

Publication Date: 2004/12/01

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Type of Manuscript: LETTER

Category: Biocybernetics, Neurocomputing

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Rameswar DEBNATH, Haruhisa TAKAHASHI, "Kernel Selection for the Support Vector Machine" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 12, pp. 2903-2904, December 2004, doi: .
Abstract: The choice of kernel is an important issue in the support vector machine algorithm, and the performance of it largely depends on the kernel. Up to now, no general rule is available as to which kernel should be used. In this paper we investigate two kernels: Gaussian RBF kernel and polynomial kernel. So far Gaussian RBF kernel is the best choice for practical applications. This paper shows that the polynomial kernel in the normalized feature space behaves better or as good as Gaussian RBF kernel. The polynomial kernel in the normalized feature space is the best alternative to Gaussian RBF kernel.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e87-d_12_2903/_p

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@ARTICLE{e87-d_12_2903,
author={Rameswar DEBNATH, Haruhisa TAKAHASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Kernel Selection for the Support Vector Machine},
year={2004},
volume={E87-D},
number={12},
pages={2903-2904},
abstract={The choice of kernel is an important issue in the support vector machine algorithm, and the performance of it largely depends on the kernel. Up to now, no general rule is available as to which kernel should be used. In this paper we investigate two kernels: Gaussian RBF kernel and polynomial kernel. So far Gaussian RBF kernel is the best choice for practical applications. This paper shows that the polynomial kernel in the normalized feature space behaves better or as good as Gaussian RBF kernel. The polynomial kernel in the normalized feature space is the best alternative to Gaussian RBF kernel.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - Kernel Selection for the Support Vector Machine
T2 - IEICE TRANSACTIONS on Information
SP - 2903
EP - 2904
AU - Rameswar DEBNATH
AU - Haruhisa TAKAHASHI
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2004
AB - The choice of kernel is an important issue in the support vector machine algorithm, and the performance of it largely depends on the kernel. Up to now, no general rule is available as to which kernel should be used. In this paper we investigate two kernels: Gaussian RBF kernel and polynomial kernel. So far Gaussian RBF kernel is the best choice for practical applications. This paper shows that the polynomial kernel in the normalized feature space behaves better or as good as Gaussian RBF kernel. The polynomial kernel in the normalized feature space is the best alternative to Gaussian RBF kernel.
ER -