# 相关向量机

$k(\mathbf{x},\mathbf{x'}) = \sum_{j=1}^N \frac{1}{\alpha_j} \phi(\mathbf{x},\mathbf{x}_j)\phi(\mathbf{x}',\mathbf{x}_j)$

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]