Title: Gaussian process classification using posterior linearisation,
Abstract: This paper proposes a new algorithm for Gaussian process classification based
on posterior linearisation (PL). In PL, a Gaussian approximation to the
posterior density is obtained iteratively using the best possible linearisation
of the conditional mean of the labels and accounting for the linearisation
error. Considering three widely-used likelihood functions, in general, PL
provides lower classification errors in real data sets than expectation
propagation and Laplace algorithms.