您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(理学版)》

J4 ›› 2012, Vol. 47 ›› Issue (7): 50-54.

• 计算机科学 • 上一篇    下一篇

基于Fisher准则的SVM参数选择算法

刘飚1,2,陈春萍3,封化民1,3,李洋3   

  1. 1. 北京电子科技学院管理系, 北京 100070; 2. 北京邮电大学电子工程学院, 北京 100876;
    3. 西安电子科技大学通信工程学院, 陕西 西安 710071
  • 收稿日期:2011-11-30 出版日期:2012-07-20 发布日期:2012-09-01
  • 作者简介:刘飚(1981- ),男,博士研究生,主要研究方向为多媒体信息处理、网络安全等。Email:liubiao@besti.edu.cn
  • 基金资助:

    国家自然科学基金资助项目(60972139);中央高校基本科研业务费专项资金资助项目(YZDJ1105)

A SVM parameters selection algorithm based on Fisher criterion

LIU Biao1,2, CHEN Chun-ping3, FENG Hua-min1,3, LI Yang3   

  1. 1. Department of Management, Beijing Electronic Science and Technology Institute, Beijing 100070, China;
    2. School of Electronic Engineering, Beijing University of Posts and Communications, Beijing 100876, China;
    3. School of Communication Engineering, Xidian University, Xi′an 710071, Shaanxi, China
  • Received:2011-11-30 Online:2012-07-20 Published:2012-09-01

摘要:

 支持向量机(support vector machine,SVM)分类性能主要受到SVM模型选择(包括核函数的选择和参数的选取)的影响,目前SVM模型参数选择的方法并不能较好地确定模型参数。为此基于Fisher准则提出了SVM参数选择算法。该算法利用样本在特征空间中的类别间的线性可分离性,结合梯度下降算法进行参数寻优,并基于Matlab实现选择算法。实验结果表明参数选择算法既提高了SVM训练性能,又大大减少了训练时间。

关键词: 核函数;支持向量机;Fisher准则;梯度下降算法

Abstract:

SVM (support vector machine) classification performance is mainly influenced by the SVM model selection (including the choice of the kernel function and parameters selected). It is not better to determine the SVM model parameters by the existing methods of SVM model parameter selection. Therefore a SVM parameter selection algorithm is presented based on the Fisher criterion. The selection algorithm makes full use of the samples of linear separability in the classes in the feature space, and combines with the gradient descent algorithm for parameter optimization. It is realized by Matlab. The experimental results show that this parameter selection algorithm not only improves the training performance of SVM, but also greatly reduces the training time through the simulation.

Key words: kernel function; SVM; Fisher criterion; gradient descent algorithm

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!