Name
Abstract | ||
|---|---|---|
•We proposed a method for approximating unknown target function using large sample sets with noises.•We proposed a mathematical framework for sequential approximation (SA) method, which allows us to define the method in a unified manner.•We extend the analysis of the SA method to noisy data case, which was not considered by the previous work.•We provided extensive numerical examples to demonstrate the performance of the method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jcp.2018.05.042 | Journal of Computational Physics |
Keywords | Field | DocType |
Approximation theory,Randomized algorithm,Noisy data | Randomized algorithm,Mathematical optimization,Noisy data,Function approximation,Upper and lower bounds,Approximation theory,Algorithm,Sequential method,Mathematics,Vector operations | Journal |
Volume | ISSN | Citations |
371 | 0021-9991 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yeonjong Shin | 1 | 2 | 0.75 |
| Kailiang Wu | 2 | 7 | 1.80 |
| Dongbin Xiu | 3 | 1068 | 115.57 |