Approximation of Gaussian Process Regression Models after Training

Abstract. The evaluation of a standard Gaussian process regression model takes time linear in the number of training data points. In this paper, the models are approximated in the feature space after training. It is empirically shown that the time required for evaluation can be drastically reduced without considerable loss in performance.

Approximation of Gaussian Process Regression Models after Training

Abstract. The evaluation of a standard Gaussian process regression model takes time linear in the number of training data points. In this paper, the models are approximated in the feature space after training. It is empirically shown that the time required for evaluation can be drastically reduced without considerable loss in performance.