%0 Conference Paper %1 conf/icml/RaykarYZJFVBM09 %A Raykar, Vikas C. %A Yu, Shipeng %A Zhao, Linda H. %A Jerebko, Anna K. %A Florin, Charles %A Valadez, Gerardo Hermosillo %A Bogoni, Luca %A Moy, Linda %B ICML %D 2009 %E Danyluk, Andrea Pohoreckyj %E Bottou, Léon %E Littman, Michael L. %I ACM %K %P 889-896 %R https://doi.org/10.1145/1553374.1553488 %T Supervised learning from multiple experts: whom to trust when everyone lies a bit. %U https://dl.acm.org/doi/10.1145/1553374.1553488 %V 382 %X We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline. %@ 978-1-60558-516-1

@inproceedings{conf/icml/RaykarYZJFVBM09, abstract = { We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline. }, added-at = {2023-12-12T18:00:08.000+0100}, author = {Raykar, Vikas C. and Yu, Shipeng and Zhao, Linda H. and Jerebko, Anna K. and Florin, Charles and Valadez, Gerardo Hermosillo and Bogoni, Luca and Moy, Linda}, biburl = {https://www.bibsonomy.org/bibtex/293f689afbc66b6a78af4423540b01087/admin}, booktitle = {ICML}, crossref = {conf/icml/2009}, doi = {https://doi.org/10.1145/1553374.1553488}, editor = {Danyluk, Andrea Pohoreckyj and Bottou, Léon and Littman, Michael L.}, ee = {https://doi.org/10.1145/1553374.1553488}, interhash = {0bf4c2fb6fc24842ba67c9d7e96bdd87}, intrahash = {93f689afbc66b6a78af4423540b01087}, isbn = {978-1-60558-516-1}, keywords = {}, pages = {889-896}, publisher = {ACM}, series = {ACM International Conference Proceeding Series}, timestamp = {2023-12-12T18:00:08.000+0100}, title = {Supervised learning from multiple experts: whom to trust when everyone lies a bit.}, url = {https://dl.acm.org/doi/10.1145/1553374.1553488}, volume = 382, year = 2009 }