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相关向量机

2018-08-24 13:59:09     所属分类:机器学习

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。:

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。[来源请求]

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.[来源请求]

参考

  • Tipping, Michael E. Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research. 2001, 1: 211–244. doi:10.1162/15324430152748236. 

软件

  • dlib C++ Library
  • The Kernel-Machine Library

外部链接

  • Tipping's webpage on Sparse Bayesian Models and the RVM
  • A Tutorial on RVM by Tristan Fletcher

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