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。：

$k(\mathbf {x} ,\mathbf {x'} )=\sum _{j=1}^{N}{\frac {1}{\alpha _{j}}}\phi (\mathbf {x} ,\mathbf {x} _{j})\phi (\mathbf {x} ',\mathbf {x} _{j})$

其中φ是核函数（通常是高斯核函数），x₁,…,x_N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

• Tipping, Michael E. Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research. 2001, 1: 211–244 [2010-03-31]. doi:10.1162/15324430152748236. （原始内容存档于2020-02-19）. 

软件

• dlib C++ Library （页面存档备份，存于互联网档案馆）
• The Kernel-Machine Library （页面存档备份，存于互联网档案馆）

外部链接

• Tipping's webpage on Sparse Bayesian Models and the RVM （页面存档备份，存于互联网档案馆）
• A Tutorial on RVM by Tristan Fletcher