Kernel Selection for the Support Vector Machine(Biocybernetics, Neurocomputing)

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概要

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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 ker 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.

• 社団法人電子情報通信学会の論文
• 2004-12-01

著者

• Takahashi Haruhisa

  Department Of Communications And Systems Engineering The University Of Electro-communications

• Debnath Rameswar

  Department Of Information And Communication Engineering The University Of Electro-communications

関連論文

• Implementation Issues of Second-Order Cone Programming Approaches for Support Vector Machine Learning Problems
• Kernel Selection for the Support Vector Machine(Biocybernetics, Neurocomputing)
• A New Approach to the Structural Learning of Neural Networks(Neural Networks and Bioengineering)
• Learning Curves in Learning with Noise : An Empirical Study
• Estimating Learning Curves of Concept Learning

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