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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

DOI: 10.3724/SP.J.1004.2011.00932, PP. 932-943

Keywords: 特征选择,稀疏化,相关向量机,Probit模型,变分贝叶斯

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Abstract:

?相关向量机(Relevancevectormachine,RVM)是一种函数形式等价于支持向量机(Supportvectormachine,SVM)的全概率模型,利用变分贝叶斯(VariationalBayesian,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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