相关向量机

相关向量机（Relevance vector machine，RVM）是使用贝叶斯推理得到回归和分类的简约解的机器学习技术。RVM的函数形式与支持向量机相同，但是可以提供概率分类。

其与带协方差函数的高斯过程等效。：

其中φ是核函数（通常是高斯核函数），x1,…,xN是训练集的输入向量。

Compared to the SVM the Bayesian formulation allows avoiding the set of free parameters that the SVM has and that usually require cross-validation based post optimizations. However RVMs use an Expectation Maximization (EM)-like learning method and are therefore at risk of local minima, unlike the standard SMO-based algorithms employed by SVMs which are guaranteed to find a global optimum.

 参考 


 软件
 dlib C++ Library 
 The Kernel-Machine Library  

外部链接
Tipping's webpage on Sparse Bayesian Models and the RVM 
A Tutorial on RVM by Tristan Fletcher

Category:分類演算法
Category:Ensemble learning
Category:机器学习
Category:Non-parametric Bayesian methods
Category:核方法