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| 基于Fisher准则的SVM参数选择算法 | |
| 引用本文： | 刘飚,陈春萍,封化民,李洋.基于Fisher准则的SVM参数选择算法[J].山东大学学报(理学版),2012,47(7):50-54,69. |
| 作者姓名： | 刘飚 陈春萍 封化民 李洋 |
| 作者单位： | 1. 北京电子科技学院管理系,北京100070 北京邮电大学电子工程学院,北京100876 2. 西安电子科技大学通信工程学院,陕西西安,710071 3. 北京电子科技学院管理系,北京100070 西安电子科技大学通信工程学院,陕西西安710071 |
| 基金项目： | 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) |
| 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 |
A SVM parameters selection algorithm based on Fisher criterion |
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| LIU Biao,CHEN Chun-ping,FENG Hua-min,LI Yang.A SVM parameters selection algorithm based on Fisher criterion[J].Journal of Shandong University,2012,47(7):50-54,69. | |
| Authors: | LIU Biao CHEN Chun-ping FENG Hua-min LI Yang |
| Institution: | 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) |
| 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. |
| Keywords: | kernel function SVM Fisher criterion gradient descent algorithm |
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