28

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.

What are the assumptions made (if any) in RVMs, and what is the general optimization problem?

mhdadk
  • 2,582
  • 1
  • 4
  • 17
Firebug
  • 15,262
  • 5
  • 60
  • 127
  • 2
    I think these [lecture slides](https://www.cc.gatech.edu/~hic/CS7616/pdf/lecture9.pdf) help in understanding the differences. – MotiNK Aug 21 '18 at 12:13
  • The question was worded very clearly imo, I see no point in editing it. Two main questions are "How does the RVM achieve greater sparsity and automatic parameter selection?" and "How is it formulated as a Gaussian Process and what makes it different?", this has been written this way since I posted it. Voting to reopen. – Firebug Apr 16 '21 at 22:40
  • 3
    I would support reopening a slightly narrower version, because in its present form you ask two major questions. – whuber May 11 '21 at 20:51
  • 1
    To be frank, at the time when I wrote this question I didn't fully grasp it, but now after so much time I am entertaining writing an answer of my own. I'll wait for this question's 5th anniversary by the end of the month. – Firebug Sep 03 '21 at 12:28

0 Answers0