Name
Abstract | ||
|---|---|---|
•State-of-the-art MOGPs were reviewed and analyzed.•Ten representative MOGPs were investigated in different scenarios.•Heterotopic training data enlarges information diversity for symmetric MOGPs.•Symmetric MOGPs favor moderate training size, while asymmetric MOGPs favor small and moderate training sizes.•Decomposed process helps asymmetric MOGPs perform well for complex cases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.knosys.2017.12.034 | Knowledge-Based Systems |
Keywords | Field | DocType |
Multi-output Gaussian process,Symmetric/asymmetric MOGP,Multi-fidelity,Output correlation,Knowledge transfer | High fidelity,Kriging,Data mining,Information transfer,Hyperparameter,Computer science,Inference,Separable space,Gaussian process,Covariance | Journal |
Volume | ISSN | Citations |
144 | 0950-7051 | 14 |
PageRank | References | Authors |
0.67 | 37 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haitao Liu | 1 | 44 | 8.15 |
| jianfei cai | 2 | 1804 | 147.18 |
| Yew-Soon Ong | 3 | 263 | 23.35 |