Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

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

Authors
Wei Wang, Santong Zhang, Wei Yang, Xiangbin Liu
Corresponding Author
Wei Wang
Available Online February 2018.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
February 2018
ISBN
978-94-6252-487-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/csece-18.2018.86How to use a DOI?
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  -