检索
E-mail
RSS
作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2009, Vol. 35 ›› Issue (14): 218-220.doi: 10.3969/j.issn.1000-3428.2009.14.076

• 人工智能及识别技术 • 上一篇    下一篇

支持向量机回归的参数选择方法

闫国华，朱永生   

  1. (西安交通大学机械工程学院，西安 710049)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-07-20 发布日期:2009-07-20

Parameters Selection Method for Support Vector Machine Regression

YAN Guo-hua, ZHU Yong-sheng   

  1. (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-07-20 Published:2009-07-20

PDF

6212

被引次数

105 | 21

摘要： 综合4种支持向量机回归的参数选择方法的优点，提出一种对训练样本进行分析并直接确定参数的方法。在标准测试数据集上的试验证明，该方法与传统网格搜索法相比，在时间和预测精度方面取得了更好的结果，可以较好地解决支持向量机在实际应用中参数难以选择、消耗时间长的问题。

关键词: 支持向量机, 回归, 参数选择

Abstract: By combining several parameters selection approaches of Support Vector Machine(SVM), this paper proposes a method that defines parameters directly by analyzing training samples. Experimental results based on several standard test data sets show that the method achieves better prediction accuracy and consumes less time compared to traditional grid search methods. It provides one way to deal with the problem of selecting parameters and time consuming in application of SVM.

Key words: Support Vector Machine(SVM), regression, parameters selection

中图分类号: