Name
Abstract | ||
|---|---|---|
Multi-task learning is an important trend of machine learning in facing the era of artificial intelligence and big data. Despite a large amount of researches on learning rate estimates of various single-task machine learning algorithms, there is little parallel work for multi-task learning. We present mathematical analysis on the learning rate estimate of multi-task learning based on the theory of vector-valued reproducing kernel Hilbert spaces and matrix-valued reproducing kernels. For the typical multi-task regularization networks, an explicit learning rate dependent both on the number of sample data and the number of tasks is obtained. It reveals that the generalization ability of multi-task learning algorithms is indeed affected as the number of tasks increases. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.09.031 | Neurocomputing |
Keywords | DocType | Volume |
Vector-valued reproducing kernel Hilbert spaces,Multi-task learning,Matrix-valued reproducing kernels,Learning rates,Regularization networks | Journal | 466 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Gui | 1 | 67 | 6.08 |
| Haizhang Zhang | 2 | 126 | 16.42 |