Scalable and efficient learning from crowds with Gaussian processes
Information Fusion(2019)
摘要
•Two novel scalable and efficient methods to learn from crowds are proposed.•Their computational training cost scales up linearly with the training set size.•Their computational test cost is independent on the training set size.•They are applied in previously-prohibitive datasets and exhibit great performance.•Both approaches accurately estimate and fuse the expertise of all the annotators.
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关键词
Scalable crowdsourcing,Classification,Gaussian processes,Fourier features,Bayesian modelling,Variational inference
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