Mixtures of Gaussian process priors
J. C. Lemm · 1999
Mixtures of Gaussian process priors allow the flexible implementation of complex and situation specific a priori information. This is essential for tasks with, compared to their complexity, small number of available training data. The paper concentrates on the formalism for Gaussian regression problems where prior mixture models provide a generalisation of classical quadratic, typically smoothness related, regularisation approaches being more flexible without having a much larger computational complexity. 1 Introduction The generalisation behaviour of statistical learning algorithms relies essentially on the correctness of the implemented a priori information. While Gaussian processes and the related regularisation approaches have, on one hand, the very important advantage of being able to formulate a priori information explicitly in terms of the function of interest (mainly in the form of smoothness priors which have a long tradition in density estimation and regression problems [7, ...