Designing active learning algorithms for a crowd-sourced database poses many practical challenges: such algorithms need to be generic, scalable, and easy to use ...

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

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

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

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 · ... Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning". Subjects: Machine Learning (cs.LG); Databases (cs.DB). Cite as ...

Scaling up crowd-sourcing to very large datasets: a case for active learning ... Visualization-aware sampling for very large databases. Y Park, M Cafarella ...

Scaling up crowd-sourcing to very large datasets: a case for active learning ... Visualization-aware sampling for very large databases. Y Park, M Cafarella ...

... to combine humans and algorithms together in a crowd-sourced database ... Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning.