  We present an extension to active learning routines in which non-constant
costs are explicitly considered. This work considers both known and unknown
costs and introduces the term \epsilon-frugal for learners that do not only
consider minimizing total costs but are also able to explore high cost regions
of the sample space. We demonstrate our extension on a well-known machine
learning dataset and find that out \epsilon-frugal learners outperform both
learners with known costs and random sampling.
