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 ...
People also ask
How do you handle large datasets in deep learning?
How do you manage large sets of data?
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 · ... Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning". Subjects: Machine Learning (cs.LG); Databases (cs.DB). Cite as ...
Jan 13, 2024 · This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling ...
The easiest way to keep your publications page up to date.
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.