Active learning differs from passive "learning from examples" in that
the learning algorithm itself attempts to select the most informative data for
training. Since supervised labeling of data is expensive, active learning
attempts to reduce the human effort needed to learn an accurate result by
selecting only the most informative examples for labeling. Our work has
focused on diverse
ensembles for active learning
and applications of active learning to problems in
natural-language processing and
semi-supervised learning. We have also addressed the problem of actively
acquiring the most useful
features values of examples as well as supervised
class labels.