Exact learning curves for Gaussian process regression on large random graphs

We study learning curves for Gaussian process regression which characterise performance in terms of the Bayes error averaged over datasets of a given size. Whilst learning curves are in general very difficult to calculate we show that for discrete input domains, where similarity between input points is characterised in terms of a graph, accurate predictions can be obtained... (read more)

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METHOD TYPE
Gaussian Process
Non-Parametric Classification