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 $\gamma$-power divergence. As a particular class of the Bregman divergence, the $\beta$-divergence is considered. By deriving the influence function, we show that the proposed method using $\beta$-divergence and dual $\gamma$-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.
翻译:积极学习是用于测量成本高的各类问题的一种广泛使用的方法。 在积极学习中, 下一个要测量的对象由获取功能选择, 并按顺序进行测量。 委员会的查询是一个众所周知的获取功能。 在常规方法中, 委员会的分歧用Kullback- Leiber 差异量化。 在本文中, 分歧的衡量尺度由Bregman 差异定义, 包括 Kullback- Lebeler 差异, 以及 $\gmac- power 差异。 作为布雷格曼差异的一个特定类别, 考虑$\beta$- diverence 。 通过得出影响函数, 我们显示, 拟议的方法使用 $\beta$- diverence 和 $\gammac- power 差异比 Kullback- Lebeler 差异定义分歧的常规方法更强。 实验结果显示, 拟议的方法的表现和优于常规方法。