During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted Change of F Measure will be introduced that attempts to provide the users an estimate of how much performance of the model is changing at each iteration. This stopping method can be applied with any base learner. This method is useful for reducing the data annotation bottleneck encountered when building text classification systems.

翻译：在积极学习期间,有效的停止方法使用户能够限制说明的数量,这是符合成本效益的。本文件将采用一种新的停止方法,称为F度量的预测变化,试图向用户提供模型在每次迭代时多少性能变化的估计。这种停止方法可以适用于任何基础学习者。这种方法有助于减少在建立文本分类系统时遇到的数据注解瓶颈。