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一种基于变分相关向量机的特征选择和分类结合方法

徐丹蕾 杜兰 刘宏伟 洪灵 李彦兵

徐丹蕾, 杜兰, 刘宏伟, 洪灵, 李彦兵. 一种基于变分相关向量机的特征选择和分类结合方法. 自动化学报, 2011, 37(8): 932-943. doi: 10.3724/SP.J.1004.2011.00932
引用本文: 徐丹蕾, 杜兰, 刘宏伟, 洪灵, 李彦兵. 一种基于变分相关向量机的特征选择和分类结合方法. 自动化学报, 2011, 37(8): 932-943. doi: 10.3724/SP.J.1004.2011.00932
XU Dan-Lei, DU Lan, LIU Hong-Wei, HONG Ling, LI Yan-Bing. Joint Feature Selection and Classification Design Based on Variational Relevance Vector Machine. ACTA AUTOMATICA SINICA, 2011, 37(8): 932-943. doi: 10.3724/SP.J.1004.2011.00932
Citation: XU Dan-Lei, DU Lan, LIU Hong-Wei, HONG Ling, LI Yan-Bing. Joint Feature Selection and Classification Design Based on Variational Relevance Vector Machine. ACTA AUTOMATICA SINICA, 2011, 37(8): 932-943. doi: 10.3724/SP.J.1004.2011.00932

一种基于变分相关向量机的特征选择和分类结合方法

doi: 10.3724/SP.J.1004.2011.00932
详细信息
    通讯作者:

    杜兰 西安电子科技大学电子工程学院教授. 2007年获得西安电子科技大学信息与通信工程博士学位, 主要研究方向为统计信号处理、雷达信号处理、机器学习及其在雷达目标检测与识别方面的应用.本文通信作者.E-mail: dulan@mail.xidian.edu.cn

Joint Feature Selection and Classification Design Based on Variational Relevance Vector Machine

  • 摘要: 相关向量机(Relevance vector machine, RVM)是一种函数形式等价于支持向量机(Support vector machine, SVM)的全概率模型,利用变分贝叶斯(Variational Bayesian, VB)方法求解的RVM可以给出所有参数的后验分布. 进一步,通过对样本所在原始特征空间的稀疏化,基于线性核的RVM可以在分类的同时实现对原始特征的线性选择. 本文在传统VB-RVM的基础上提出一种特征选择和分类结合方法. 该方法采用Probit模型将分类问题与回归问题有机地结合起来, 同时,通过对特征维的幂变换扩展,不仅在分类时增加了样本的信息量, 可以构造非线性分类面,而且实现了非线性特征选择的功能. 通过对仿真数据和实测数据分别进行实验, 证明了该特征选择和分类结合方法的实用性和有效性.
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  • 收稿日期:  2010-09-16
  • 修回日期:  2011-02-01
  • 刊出日期:  2011-08-20

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