Name
Title | ||
|---|---|---|
Accurate Interpretation of the Online Learning Model for 6G-Enabled Internet of Things |
Abstract | ||
|---|---|---|
The next-generation network (6G) has more strict requirements for the online learning ability and high interpretability of the learned systems. Machine learning is expected to be essential to assist in making the networks efficient and adaptable, but most promising methods often are treated as “black boxes” due to the deep structures and high nonlinearity. Therefore, this article attempts to study... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JIOT.2020.3048793 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Artificial neural networks,Internet of Things,Decision trees,6G mobile communication,Machine learning algorithms,Data models,Machine learning | Journal | 8 |
Issue | ISSN | Citations |
20 | 2327-4662 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinchao Huang | 1 | 0 | 2.37 |
| Guofu Li | 2 | 0 | 0.68 |
| Jianwei Tian | 3 | 0 | 0.68 |
| Shenghong Li | 4 | 0 | 4.73 |