计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (30): 72-73.DOI: 10.3778/j.issn.1002-8331.2008.30.021

• 理论研究 • 上一篇    下一篇

一种推广高斯核模型设计及其优化

韩 虎1,2,任恩恩2   

  1. 1.兰州交通大学 数理与软件工程学院,兰州 730070
    2.兰州交通大学 光电技术与智能控制教育部重点实验室,兰州 730070
  • 收稿日期:2007-11-28 修回日期:2008-02-18 出版日期:2008-10-21 发布日期:2008-10-21
  • 通讯作者: 韩 虎

Design and optimization of generalized Gaussian kernel

HAN Hu1,2,REN En-en2   

  1. 1.College of Mathematics,Physics & Software Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
    2.Key Laboratory Opto-Electronic Technology and Intelligent Control,Lanzhou Jiaotong University,Lanzhou 730070,China
  • Received:2007-11-28 Revised:2008-02-18 Online:2008-10-21 Published:2008-10-21
  • Contact: HAN Hu

摘要: 支持向量机分类中,不同属性对分类的贡献往往不同,针对此问题,在核函数中引入属性权重,提出一种推广的高斯核模型,同时以最小化k-fold交叉验证误差为目标,采用粒子群算法进行推广高斯核的模型选择。最后通过UCI上标准数据集进行实验,证实该方法能够有效提高支持向量机的推广能力。

关键词: 支持向量机, 属性权重, 推广高斯核, 交叉验证, 粒子群优化

Abstract: In support vector classification,different features effect differently on classification.For this problem,a generalized Gaussian kernel function is proposed by using features weight,particle swarm optimization is used to do the model selection with the k-fold cross-validation error as the fitness.Then experiment results on UCI dataset show that the method can improve the performance of SVM.

Key words: support vector machine, feature weight, generalized Gaussian kernel, cross validation, particle swarm optimization