  Active learning is a framework for supervised learning to improve the
predictive performance by adaptively annotating a small number of samples. To
realize efficient active learning, both an acquisition function that determines
the next datum and a stopping criterion that determines when to stop learning
should be considered. In this study, we propose a stopping criterion based on
error stability, which guarantees that the change in generalization error upon
adding a new sample is bounded by the annotation cost and can be applied to any
Bayesian active learning. We demonstrate that the proposed criterion stops
active learning at the appropriate timing for various learning models and real
datasets.
