Sciweavers

Share
ICML
2009
IEEE

Supervised learning from multiple experts: whom to trust when everyone lies a bit

11 years 5 months ago
Supervised learning from multiple experts: whom to trust when everyone lies a bit
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.
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ICML
Authors Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy
Comments (0)
books