Process for machine learning through Gaussian processes

摘要

Computer-implemented method for machine learning through Gaussian processes, GP, with the steps of selecting an accuracy target, selecting a prior GP family that is parameterized by hyperparameters, obtaining a training data set, selecting the GP parameterization for modeling, training the GPs by optimizing a PAC-Bayes barrier using the training data set, the Prior GP family, and the accuracy target, and predictions of the next expected values by the trained GPs.