Name
Abstract | ||
|---|---|---|
In this paper, we propose a weight-time-dependent (WTD) update approach for an online EM algorithm applied to the Normalized Gaussian network (NGnet). WTD aims to improve a recently proposed weight-dependent (WD) update approach by Celaya and Agostini. First, we discuss the derivation of WD from an older time-dependent (TD) update approach. Then, we consider additional aspects to improve WD, and by including them we derive the new WTD approach from TD. The difference between WD and WTD is discussed, and some experiments are conducted to demonstrate the effectiveness of the proposed approach. WTD succeeds in improving the learning performance for a function approximation task with balanced and dynamic data distributions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46681-1_64 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Normalized Gaussian networks,Online EM,Local model,Weight-dependent forgetting | Normalization (statistics),Pattern recognition,Function approximation,Computer science,Expectation–maximization algorithm,Gaussian,Dynamic data,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
9950 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jana Backhus | 1 | 0 | 1.01 |
| Ichigaku Takigawa | 2 | 209 | 18.15 |
| Hideyuki Imai | 3 | 103 | 25.08 |
| Mineichi Kudo | 4 | 927 | 116.09 |
| Masanori Sugimoto | 5 | 775 | 95.39 |