A Step-by-Step Introduction to RVMs
This tutorial paper has been written to make Tipping's Relevance Vector Machines (RVMs) as simple to understand as possible for those with minimal experience of Machine Learning. It assumes knowledge of probability in the areas of Bayes' theorem and Gaussian distributions including marginal and conditional Gaussian distributions. It also assumes familiarity with matrix differentiation, the vector representation of regression and kernel (basis) functions.
What the Tutorial Covers
- Bayesian inference and the evidence framework
- How RVMs achieve sparsity compared to SVMs
- The relevance vector and automatic relevance determination
- Kernel functions and basis function selection
- Practical implementation considerations
Download the full tutorial (PDF)
Written by Dr Tristan Fletcher. See also the companion tutorials on Support Vector Machines and the Kalman Filter, or browse all ML tutorials.