Summary: Active learning and semi-supervised learning are two approaches to alleviate the burden of labeling large amounts of data. In active learning, user is asked to label the most informative examples in the domain. In semi-supervised learning, labeled data is used together with unlabeled data to boost the performance of learning algorithms. We focus here to combine them together. We first introduce a new active learning strategy, then we propose an algorithm to take the advantage of both active learning and semi-supervised learning. We discuss several advantages of our method. Experimental results show that it is efficient and robust to noise.