Optimal Gaussian Kernel Parameter Selection for SVM Classifier

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概要

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• The performance of the kernel-based learning algorithms, such as SVM, depends heavily on the proper choice of the kernel parameter. It is desirable for the kernel machines to work on the optimal kernel parameter that adapts well to the input data and the learning tasks. In this paper, we present a novel method for selecting Gaussian kernel parameter by maximizing a class separability criterion, which measures the data distribution in the kernel-induced feature space, and is invariant under any non-singular linear transformation. The experimental results show that both the class separability of the data in the kernel-induced feature space and the classification performance of the SVM classifier are improved by using the optimal kernel parameter.

• 2010-12-01

著者

• Yang Xu

  Beijing Jiaotong Univ. Beijing Chn

• Yang Xin

  Institute For Chemometrics And Chemical Sensing Technology College Of Chemistry And Chemical Enginee

• Yang Xu

  Institute Of Computer Science And Engineering Beijing Jiaotong University

• Yang Xu

  Institute Of Image Processing & Pattern Recognition Shanghai Jiaotong University

• XIONG HuiLin

  Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University

• Xiong Huilin

  Institute Of Image Processing & Pattern Recognition Shanghai Jiaotong University

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