Learning with an unreliable teacher
Pattern Recognition
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
Supervised learning from multiple experts: whom to trust when everyone lies a bit
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficiently learning the accuracy of labeling sources for selective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating classifiers by means of test data with noisy labels
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning from multiple annotators with Gaussian processes
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Computer Vision and Image Understanding
Semi-supervised learning for integration of aerosol predictions from multiple satellite instruments
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In regression learning, it is often difficult to obtain the true values of the label variables, while multiple sources of noisy estimates of lower quality are readily available. To address this problem, we propose a new Bayesian approach that learns a regression model from data with noisy labels provided by multiple oracles. The proposed method provides closed form solution for model parameters and is applicable to both linear and nonlinear regression problems. In our experiments on synthetic and benchmark datasets this new regression model was consistently more accurate than a model trained with averaged estimates from multiple oracles as labels.