Generative Models for Learning from Crowds

Abstract

In this paper, we propose generative probabilistic models for label aggregation. We use Gibbs sampling and a novel variational inference algorithm to perform the posterior inference. Empirical results show that our methods consistently outperform state-of-the-art methods.

References in corpus (1)

• Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing