Name
Abstract | ||
|---|---|---|
This approach described in this article uses machine learning to execute and observe a sample of software configurations within a sample of contexts. It then learns what factors of each context will likely discard or activate some of the software's features. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MS.2017.4121211 | IEEE Software |
Keywords | DocType | Volume |
Machine learning,Context modeling,Noise level,Computational modeling,Numerical models,Feature extraction,Cameras | Journal | 34 |
Issue | ISSN | Citations |
6 | 0740-7459 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Temple | 1 | 3 | 1.72 |
| Mathieu Acher | 2 | 747 | 52.36 |
| Jean-Marc Jézéquel | 3 | 3050 | 219.89 |
| Olivier Barais | 4 | 724 | 61.99 |