Remarks on multi-output Gaussian process regression
Multi-output regression problems have extensively arisen in modern engineering community. This article investigates the state-of-the-art multi-output Gaussian processes (MOGPs) that can transfer the knowledge across related outputs in order to improve prediction quality. We classify existing MOGPs i...
Saved in:
Main Authors: | Liu, Haitao, Cai, Jianfei, Ong, Yew-Soon |
---|---|
其他作者: | School of Computer Science and Engineering |
格式: | Article |
語言: | English |
出版: |
2020
|
主題: | |
在線閱讀: | https://hdl.handle.net/10356/139612 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
機構: | Nanyang Technological University |
語言: | English |
相似書籍
-
Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method
由: Liu, Haitao, et al.
出版: (2020) -
Understanding and comparing scalable Gaussian process regression for big data
由: Liu, Haitao, et al.
出版: (2020) -
When Gaussian process meets big data : a review of scalable GPs
由: Liu, Haitao, et al.
出版: (2021) -
Modulating scalable Gaussian processes for expressive statistical learning
由: Liu, Haitao, et al.
出版: (2022) -
Large-scale heteroscedastic regression via Gaussian process
由: Liu, Haitao, et al.
出版: (2022)