Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However, GP kernel design and the associated hyper-parameter optimization are still hard and to a large extend open problems. In this paper, …