相关向量机

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相关向量机(Relevance vector machine,RVM)是使用贝叶斯推理得到回归分类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训练集的输入向量。[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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