To scale up to large datasets, we use machine learning to avoid obtaining crowd labels for a significant portion of the data. Active Learning. AL has a rich ...

This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling with the speed ...

Oct 1, 2014 · This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling ...

This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling with the ...

This paper proposes algorithms for in- tegrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling with the speed ...

Sep 9, 2016 · This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling ...

Jan 13, 2024 · This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling ...

Sep 17, 2012 · Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database.

Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning. Mozafari, B., Sarkar, P., Franklin, M. J., Jordan, M. I., & Madden, S. Proc.

Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning · Computer Science. Proc. VLDB Endow. · 2014.