Name
Abstract | ||
|---|---|---|
Crowdsourcing labeling systems provide an efficient way to generate multiple inaccurate labels for given observations. If the competence level or the “reputation,” which can be explained as the probabilities of annotating the right label, for each crowdsourcing annotators is equal and biased to annotate the right label, majority voting (MV) is the optimal decision rule for merging the multiple lab... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TNNLS.2018.2836969 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Crowdsourcing,Labeling,Noise measurement,Reliability,Machine learning,Training,Task analysis | Data set,Optimal decision,Task analysis,Domain knowledge,Noise measurement,Computer science,Crowdsourcing,Artificial intelligence,Majority rule,Merge (version control),Machine learning | Journal |
Volume | Issue | ISSN |
30 | 1 | 2162-237X |
Citations | PageRank | References |
7 | 0.45 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dapeng Tao | 1 | 1115 | 61.57 |
| jun cheng | 2 | 851 | 69.84 |
| Zhengtao Yu | 3 | 460 | 69.08 |
| Kun Yue | 4 | 258 | 40.11 |
| Lizhen Wang | 5 | 153 | 26.16 |