Mixture of Groups for Learning in Crowdsourcing Scenarios
Francisco Mena and Ricardo Ñanculef
- Preliminary work in a conference, only group modeling
- Extended work in a journal, group modeling + individual annotators
Mena, Francisco, and Ricardo Ñanculef. "Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations." Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 24th Iberoamerican Congress, CIARP 2019, Havana, Cuba, October 28-31, 2019, Proceedings 24. Springer International Publishing, 2019.
@inproceedings{mena2019revisiting,
title={Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations},
author={Mena, Francisco and {\~N}anculef, Ricardo},
booktitle={Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 24th Iberoamerican Congress, CIARP 2019, Havana, Cuba, October 28-31, 2019, Proceedings 24},
pages={493--503},
year={2019},
organization={Springer},
doi={10.1007/978-3-030-33904-3_46}
}
this is a reference to our initial work in a conference
Mena, Francisco, Ricardo Ñanculef, and Carlos Valle. "Collective annotation patterns in learning from crowds." Intelligent Data Analysis 24.S1 (2020): 63-86.
@article{mena2020collective,
title={Collective annotation patterns in learning from crowds},
author={Mena, Francisco and {\~N}anculef, Ricardo and Valle, Carlos},
journal={Intelligent Data Analysis},
volume={24},
number={S1},
pages={63--86},
year={2020},
publisher={IOS Press},
doi={10.3233/IDA-200009}
}
this is a reference of our extended work with text+image data