相關向量機

相關向量機（Relevance vector machine，RVM）是使用貝葉斯推理得到回歸和分類的簡約解的機器學習技術。RVM的函數形式與支持向量機相同，但是可以提供概率分類。

其與帶協方差函數的高斯過程等效。：

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

其中φ是核函數（通常是高斯核函數），x₁,…,x_N是訓練集的輸入向量。

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.

參考

• 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. （原始內容存檔於2020-02-19）. 

軟體

• dlib C++ Library （頁面存檔備份，存於網際網路檔案館）
• The Kernel-Machine Library （頁面存檔備份，存於網際網路檔案館）

外部連結

• Tipping's webpage on Sparse Bayesian Models and the RVM （頁面存檔備份，存於網際網路檔案館）
• A Tutorial on RVM by Tristan Fletcher