Analysis of Sparse Bayesian Learning
Faul, Anita C., Tipping, Michael E.
–Neural Information Processing Systems
The recent introduction of the 'relevance vector machine' has effectively demonstrated how sparsity may be obtained in generalised linear models within a Bayesian framework. Using a particular form of Gaussian parameter prior, 'learning' is the maximisation, with respect to hyperparameters, of the marginal likelihood of the data.
Neural Information Processing Systems
Dec-31-2002