Name
Title | ||
|---|---|---|
Optimizing Evaluation Metrics for Multitask Learning via the Alternating Direction Method of Multipliers. |
Abstract | ||
|---|---|---|
Multitask learning (MTL) aims to improve the generalization performance of multiple tasks by exploiting the shared factors among them. Various metrics (e.g., F-score, area under the ROC curve) are used to evaluate the performances of MTL methods. Most existing MTL methods try to minimize either the misclassified errors for classification or the mean squared errors for regression. In this paper, we... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCYB.2017.2670608 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Measurement,Optimization,Fasteners,Training,Cybernetics,Character recognition,Context | Square (algebra),Matrix (mathematics),Artificial intelligence,Optimization problem,Cybernetics,Mathematical optimization,Multi-task learning,Regression,Character recognition,Algorithm,Area under the roc curve,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
48 | 3 | 2168-2267 |
Citations | PageRank | References |
1 | 0.35 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ge-Yang Ke | 1 | 2 | 0.69 |
| Yan Pan | 2 | 179 | 19.23 |
| Jian Yin | 3 | 861 | 97.01 |
| Changqin Huang | 4 | 77 | 9.54 |