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Relevance Vector Machines (RVMs) are really interesting models when contrasted with the highly geometrical (and popular) SVMs.

In the light of a question like How does a Support Vector Machine (SVM) work?, and how RVMs are substantially different to SVMs, e.g. What is the difference between Informative (IVM) and Relevance (RVM) vector machines, I think this is a good question to be made.

So, shortly, how do RVMs work?

Answers could tackle the assumptions made (if any), and the general optimization problem.


The main points are:

  • How does the RVM achieve greater sparsity and automatic parameter selection?

  • How is it formulated as a Gaussian Process and what makes it different?

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    $\begingroup$ I think these lecture slides help in understanding the differences. $\endgroup$ – MotiN Aug 21 '18 at 12:13

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