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キーワード:
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要旨:
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 emphthey 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.
