PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Healing the Relevance Vector Machine through Augmentation
Carl Edward Rasmussen and Joaquin Quinonero Candela
In: International Conference on Machine Learning, 7-11 August 2005, Bonn, Germany.

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 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1189
Deposited By:Joaquin Quinonero Candela
Deposited On:23 November 2005