  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.
