Importance weighted active learning

Alina Beygelzimer, Sanjoy Dasgupta, John Langford

Abstract: We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process.