@inproceedings{3460,
  title = {Healing the Relevance Vector Machine through Augmentation},
  journal = {Proceedings of the 22nd International Conference on Machine Learning},
  abstract = {The Relevance Vector Machine (RVM) is a sparse approximate Bayesian
  kernel method. It provides full predictive distributions for test
  cases. However, the predictive uncertainties have the unintuitive
  property, that emph{they get smaller the further you move away from the
  training cases}. We give a thorough analysis. Inspired by the analogy to
  non-degenerate Gaussian Processes, we suggest augmentation to solve the
  problem. The purpose of the resulting model, RVM*, is primarily to
  corroborate the theoretical and experimental analysis. Although RVM*
  could be used in practical applications, it is no longer a truly sparse
  model. Experiments show that sparsity comes at the expense of worse
  predictive distributions.},
  pages = {689 },
  editors = {De Raedt, L. , S. Wrobel},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2005},
  author = {Rasmussen, CE. and Candela, JQ.}
}