Jiang, Xinwei

Gao, Junbin

Wang, Tianjiang

Kwan, Paul H

2010

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.

Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, v.II, p. 113-124

3642136710

9783642136719

https://hdl.handle.net/1959.11/7612

Springer

Lecture Notes in Artificial Intelligence

1

Learning Gradients with Gaussian Processes

Book Chapter

Publication