Two-step Gaussian Process Regression Improving Performance of Training and Prediction

DOI

10.2991/csece-18.2018.86How to use a DOI?

Keywords

gaussian process; regression; inducing inputs; hyperparameter

Abstract

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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
BT  - Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SP  - 403
EP  - 407
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.86
DO  - 10.2991/csece-18.2018.86
ID  - Wang2018/02
ER  -