Name
Abstract | ||
|---|---|---|
•A Hierarchical Gaussian Process Multi-task Learning (HGPMT) method.•Effectively utilizing the explicit correlation prior information among tasks.•A much lower computational complexity than the cross-covariance-based methods.•A multi-kernel learning method for learning non-stationary function.•Experiment on both toy and real-world datasets for demonstrating its superiority. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2017.09.021 | Pattern Recognition |
Keywords | Field | DocType |
GP-LVM,Multi-task learning,Feature learning,Hierarchical model | Collaborative filtering,Multi-task learning,Semi-supervised learning,Pattern recognition,Computer science,Gaussian process,Artificial intelligence,Covariance matrix,Hierarchical database model,Machine learning,Feature learning,Scalability | Journal |
Volume | Issue | ISSN |
74 | 1 | 0031-3203 |
Citations | PageRank | References |
4 | 0.39 | 36 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Li | 1 | 78 | 14.22 |
| Songcan Chen | 2 | 4148 | 191.89 |