论文标题
通过委员会通过查询积极学习，并有强大的分歧
Active Learning by Query by Committee with Robust Divergences
论文作者
论文摘要
主动学习是一种用于高测量成本的各种问题的广泛使用方法。在主动学习中，要测量的下一个对象是通过采集函数选择的，并依次执行测量。按委员会的查询是众所周知的收购职能。在常规方法中，委员会的分歧是通过kullback-leibler Divergence量化的。在本文中，分歧的度量是由Bregman Divergence定义的，Bregman Divergence包括Kullback-Leibler Divergence和双$γ$ -popper Divergence。作为布雷格曼（Bregman）的特定类别，考虑了$β$ - 差异。通过得出影响函数，我们表明使用$β$ -DDIVERGENCE和DUAL $γ$ -popper Divergence提出的方法比常规方法更强大，在该方法中，kullback-leibler Divergence定义了分歧的度量。实验结果表明，所提出的方法的性能和比常规方法更好或更好。
Active learning is a widely used methodology for various problems with high measurement costs. In active learning, the next object to be measured is selected by an acquisition function, and measurements are performed sequentially. The query by committee is a well-known acquisition function. In conventional methods, committee disagreement is quantified by the Kullback--Leibler divergence. In this paper, the measure of disagreement is defined by the Bregman divergence, which includes the Kullback--Leibler divergence as an instance, and the dual $γ$-power divergence. As a particular class of the Bregman divergence, the $β$-divergence is considered. By deriving the influence function, we show that the proposed method using $β$-divergence and dual $γ$-power divergence are more robust than the conventional method in which the measure of disagreement is defined by the Kullback--Leibler divergence. Experimental results show that the proposed method performs as well as or better than the conventional method.
