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。：

$k(𝐱,{𝐱}^{\prime })=\sum _{j=1}^N\frac{1}{{\alpha }_j}\varphi (𝐱,{𝐱}_j)\varphi ({𝐱}^{\prime },{𝐱}_j)$

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。Template:Citation needed

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.Template:Citation needed

参考

• Template:Cite journal

软件

• dlib C++ Library Template:Wayback
• The Kernel-Machine Library Template:Wayback

外部链接

• Tipping's webpage on Sparse Bayesian Models and the RVM Template:Wayback
• A Tutorial on RVM by Tristan Fletcher