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 ...

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 ...

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 ... Datasets. The format of each dataset is provided in the corresponding .desc ...

... {Scaling Up Crowd-Sourcing to Very Large Datasets: {A} Case for Active Learning}, journal = {{PVLDB}}, volume = {8}, number = {2}, pages = {125--136}, year ...

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

Adaptive blocking: Learning to scale up record linkage. In Workshop on ... Scaling up crowd-sourcing to very large datasets: a case for active learning.