May 30, 2021 · ... Scaling up crowd-sourcing to very large datasets: a case for active learning. Proc VLDB Endow 8:125–136. Article Google Scholar. Nguyen AT ...
The images were tagged using the CenHive system using the crowdsourcing approach. The bees were marked on Full HD images that were too big to serve as direct ...
main experts and crowd workers to use as training data. The hope is ... Scaling up crowd-sourcing to very large datasets: A case for active learning ...
Supervised Learning. Active Learning. • Mozafari et al. Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning. PVLDB 2014. • Gokhale et ...
Feb 15, 2020 · In most cases, large quantity of labeled data ... et al. Scaling up crowd-sourcing to very large datasets: A case for active learning ...
Jul 13, 2023 · robust empirical results on large retrieval datasets. Despite its strengths, BM25 exhibits limitations in understanding semantic or complex ...
a need to label large-scale and complex data. This has fos- tered the ... starts with a very small amount of labeled data, with very few annotations ...
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Aug 22, 2021 · To sum up, under this framework, active learning strategies include ... of active crowdsourcing learning methods over a series of datasets.
Scaling up crowd-sourcing to very large datasets: a case for active learning. B Mozafari, P Sarkar, M Franklin, M Jordan, S Madden. Proceedings of the VLDB ...
Large scale parallel data gen- eration for new language pairs requires ... tion and verification for cleaning up occasional human data entry errors ...