相關向量機(Relevance vector machine,RVM)是使用貝葉斯推理得到回歸分類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。

其與帶協方差函數高斯過程等效。:

其中φ是核函數(通常是高斯核函數),x1,…,xN訓練集的輸入向量。[來源請求]

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.[來源請求]

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