Beyond Classification – Large-scale Gaussian Process Inference and Uncertainty Prediction

Due to the massive (labeled) data available on the web, a tremendous interest in large-scale machine learning methods has emerged in the last years. Whereas, most of the work done in this new area of research focused on fast and efficient classification algorithms, we show in this paper how other aspects of learning can also be covered using massive datasets. The paper1 briefly presents techniques allowing for utilizing the full posterior obtained from Gaussian process regression (predictive mean and variance) with tens of thousands of data points and without relying on sparse approximation approaches. Experiments are done for active learning and one-class classification showing the benefits in large-scale settings.