Name
Title | ||
|---|---|---|
Learning from multiple inconsistent and dependent annotators to support classification tasks |
Abstract | ||
|---|---|---|
•We propose our LKAAR to classification tasks with multiple annotators.•LKAAR estimates the labelers' performance as a function of the input space.•LKAAR also considers inter-annotator dependencies.•The performance of the annotators is estimated using a non-parametric model.. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2020.10.045 | Neurocomputing |
Keywords | DocType | Volume |
Learning from crowds,Localized kernel alignment,Inconsistent annotators,Dependent labelers | Journal | 423 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julián Gil González | 1 | 0 | 0.34 |
| Álvaro-Ángel Orozco-Gutierrez | 2 | 0 | 1.69 |
| Andrés Álvarez-Meza | 3 | 25 | 2.46 |