›› 2011, Vol. 24 ›› Issue (5): 14-.

• 论文 • 上一篇    下一篇

一种基于RVM回归的分类方法

王立昆,杨新锋   

  1. (西安电子科技大学 电子工程学院,陕西 西安 710071)
  • 出版日期:2011-05-15 发布日期:2011-05-19
  • 作者简介:王立昆(1982-),男,硕士研究生。研究方向:智能信号处理与模式识别。

A Classification Method Based on RVM Regression

 WANG Li-Kun, YANG Xin-Feng   

  1. (School of Electronic Engineering,Xidian University,Xi'an 10071,China)
  • Online:2011-05-15 Published:2011-05-19

摘要:

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点,如无法获得概率输出,需要估计一个误差参数C,以及必须使用Mercer核函数等。相关向量机算法,克服了SVM上述缺点,RVM能获得与SVM相比拟的推广性能,并且更为稀疏。在此基础上,文中介绍了一种RVM回归用于分类的新分类方法,用RVRC来表示。并通过实验证明了它的可行性。

关键词: 相关向量机, 支持向量机, 分类, 回归, 回归用于分类

Abstract:

The support vector machine is a state-of-the-art technique for regression and classification.However,it suffers from a number of disadvantages,notably the absence of probabilistic outputs,the requirement to estimate a trade-off  parameter C and the need to utilize ‘Mercer’ kernel functions.The Relevance Vector Machine suffers from none of the above disadvantages,and obtains comparable generalization performance.The RVM requires dramatically fewer kernel functions.In this paper,we introduce a new classification method based on RVM regression,which is called RVRC.Experiments demonstrate its practicability.

Key words: RVM;SVM;classification;regression;RVRC

中图分类号: 

  • TP18