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- W3204132345 abstract "Crowdsourcing has emerged as a powerful paradigm for efficiently labeling large datasets and performing various learning tasks, by leveraging crowds of human annotators. When additional information is available about the data, constrained or semi-supervised crowdsourcing approaches that enhance the aggregation of labels from human annotators are well motivated. This work deals with constrained crowdsourced classification with instance-level constraints, that capture relationships between pairs of data. A Bayesian algorithm based on variational inference is developed, and its quantifiably improved performance, compared to unsupervised crowdsourcing, is analytically and empirically validated on several crowdsourcing datasets." @default.
- W3204132345 created "2021-10-11" @default.
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- W3204132345 date "2021-01-01" @default.
- W3204132345 modified "2023-10-16" @default.
- W3204132345 title "Bayesian Crowdsourcing with Constraints" @default.
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- W3204132345 doi "https://doi.org/10.1007/978-3-030-86523-8_33" @default.
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