  This paper proposes an active learning (AL) algorithm to solve regression
problems based on inverse-distance weighting functions for selecting the
feature vectors to query. The algorithm has the following features: (i)
supports both pool-based and population-based sampling; (ii) is not tailored to
a particular class of predictors; (iii) can handle known and unknown
constraints on the queryable feature vectors; and (iv) can run either
sequentially, or in batch mode, depending on how often the predictor is
retrained. The potentials of the method are shown in numerical tests on
illustrative synthetic problems and real-world datasets. An implementation of
the algorithm, which we call IDEAL (Inverse-Distance based Exploration for
Active Learning), is available at http://cse.lab.imtlucca.it/~bemporad/ideal.
