Two-step Gaussian Process Regression Improving Performance of Training and Prediction
Wei Wang, Santong Zhang, Wei Yang, Xiangbin Liu
Available Online February 2018.
- https://doi.org/10.2991/csece-18.2018.86How to use a DOI?
- gaussian process; regression; inducing inputs; hyperparameter
- Since Gaussian process regression (GPR) cannot feasibly be applied to big and growing data sets, this paper introduces an integration algorithm called Two-step Gaussian Process Regression (TGPR) which speeds up both training and prediction to solve the problem. First, analyze the basics behind regular GPR. Then, introduce TGPR by using the inducing inputs to optimize the regular GPR algorithm. Last, apply TGPR to a three-dimension model, the experimental results compared with regular GPR show that TGPR is faster and more accurate.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wei Wang AU - Santong Zhang AU - Wei Yang AU - Xiangbin Liu PY - 2018/02 DA - 2018/02 TI - Two-step Gaussian Process Regression Improving Performance of Training and Prediction PB - Atlantis Press SP - 403 EP - 407 SN - 2352-538X UR - https://doi.org/10.2991/csece-18.2018.86 DO - https://doi.org/10.2991/csece-18.2018.86 ID - Wang2018/02 ER -