Corpus ID: 226976539

Online Label Aggregation: A Variational Bayesian Approach.

@article{Hong2018OnlineLA,
  title={Online Label Aggregation: A Variational Bayesian Approach.},
  author={Chi Hong and Amirmasoud Ghiassi and Yichi Zhou and R. Birke and L. Chen},
  journal={arXiv: Learning},
  year={2018}
}
Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregation results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solutions that can incrementally infer true label distribution via subsets of data items. In this paper, we propose a novel online label aggregation framework, BiLA, which employs… Expand

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