The problems of variable selection and inference of statistical dependence have been addressed by modeling in the gradients learning framework based on the representer theorem. In this paper, we propose a new gradients learning algorithm in the Bayesian framework, called Gaussian Processes Gradient Learning (GPGL) model, which can achieve higher accuracy while returning the credible intervals of the estimated gradients that existing methods cannot provide. The simulation examples are used to verify the proposed algorithm, and its advantages can be seen from the experimental results. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Jiang, X., Gao, J., Wang, T., & Kwan, P. W. (2010). Learning gradients with Gaussian processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6119 LNAI, pp. 113–124). https://doi.org/10.1007/978-3-642-13672-6_12
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