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,…,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

• [http://dlib.net dlib C++ Library]
• [http://www.terborg.net/research/kml/ The Kernel-Machine Library]

外部链接
*[http://www.relevancevector.com Tipping's webpage on Sparse Bayesian Models and the RVM]
*[https://web.archive.org/web/20111005202038/http://www.tristanfletcher.co.uk/RVM%20Explained.pdf A Tutorial on RVM by Tristan Fletcher]