Easy construction of representations of multivariate functions with low-dimensional terms via Gaussian process regression kernel design
2021
We show that Gaussian process regression (GPR) allows representating multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster, much simpler to use, and more accurate.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
1
References
0
Citations
NaN
KQI