  In supervised learning, acquiring labeled training data for a predictive
model can be very costly, but acquiring a large amount of unlabeled data is
often quite easy. Active learning is a method of obtaining predictive models
with high precision at a limited cost through the adaptive selection of samples
for labeling. This paper explains the basic problem settings of active learning
and recent research trends. In particular, research on learning acquisition
functions to select samples from the data for labeling, theoretical work on
active learning algorithms, and stopping criteria for sequential data
acquisition are highlighted. Application examples for material development and
measurement are introduced.
