We consider unsupervised crowdsourcing performance based on the model wherein the responses of end-users are essentially rated according to how their responses correlate with the majority of other responses to the same subtasks/questions. In one setting, we consider an independent sequence of identically distributed crowdsourcing assignments (meta-tasks), while in the other we consider a single assignment with a large number of component subtasks. Both problems yield intuitive results in which the overall reliability of the crowd is a factor.
@article{arxiv.1110.1781,
title = {A Study of Unsupervised Adaptive Crowdsourcing},
author = {G. Kesidis and A. Kurve},
journal= {arXiv preprint arXiv:1110.1781},
year = {2011}
}