We present an efficient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the function is above and below a given threshold. We develop experiment selection methods based on entropy, misclassification rates, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are us...
