Support Vector Classification and Gaussian Kernel with Multiple Widths

CHANG Qun; WANG Xiao-long; LIN Yi-meng; WANG Xi-zhao; Daniel S YEUNG

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Support Vector Classification and Gaussian Kernel with Multiple Widths

更新时间：2025-07-16

- Support Vector Classification and Gaussian Kernel with Multiple Widths
- Acta Electronica Sinica Vol. 35, Issue 3, Pages: 484-487(2007)
- 作者机构：
  
  1. 哈尔滨工业大学计算机科学与技术学院,黑龙江,哈尔滨,150001
  2. 哈尔滨工业大学深圳研究生院媒体与生命科学实验室,广东,深圳,518055
  3. 河北大学数学与计算机科学学院,河北,保定,071002
  4. 香港理工大学计算学系,香港九龙
  5. 哈尔滨工业大学计算机科学与技术学院黑龙江哈尔滨,150001
  6. 哈尔滨工业大学深圳研究生院媒体与生命科学实验室广东深圳,518055
  7. 河北大学数学与计算机科学学院河北保定,071002
  8. 香港理工大学计算学系香港九龙
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP181
- Published：2007
- 稿件说明：
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CHANG Qun, WANG Xiao-long, LIN Yi-meng, et al. Support Vector Classification and Gaussian Kernel with Multiple Widths[J]. Acta Electronica Sinica, 2007, 35(3): 484-487.
DOI：

CHANG Qun, WANG Xiao-long, LIN Yi-meng, et al. Support Vector Classification and Gaussian Kernel with Multiple Widths[J]. Acta Electronica Sinica, 2007, 35(3): 484-487. DOI：

摘要

支持向量分类中

高斯核不区分样本中各个特征的重要性

显然各个特征对分类的贡献一般是不相同的.为了体现这种差别从而提高支持向量机的泛化性能

文中提出了多宽度高斯核的概念.多宽度高斯核增加了支持向量机的超级参数

进一步地

文中提出了多参数模型选择算法.算法利用误差界自动实现模型选择.通过实验验证了多宽度高斯核和多参数模型选择算法的有效性.

Abstract

In support vector classification

Gaussian kernel is insensitive to the differences of features.However

generally

different features function differently in classification.To improve the generalization performance of support vector machines

the Gaussian kernel with multiple widths is proposed to emphasize the different contributions of features to classification.With this kernel

the related model selection scheme is designed which can automatically tune multiple parameters for support vector machines by minimizing the error bound.The efficiencies of the proposed kernel and related model selection algorithms are validated via experiments.

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