  Active learning is a subfield of machine learning, in which the learning
algorithm is allowed to choose the data from which it learns. In some cases, it
has been shown that active learning can yield an exponential gain in the number
of samples the algorithm needs to see, in order to reach generalization error
$\leq \epsilon$. In this work we study the problem of learning halfspaces with
membership queries. In the membership query scenario, we allow the learning
algorithm to ask for the label of every sample in the input space. We suggest a
new algorithm for this problem, and prove it achieves a near optimal label
complexity in some cases. We also show that the algorithm works well in
practice, and significantly outperforms uncertainty sampling.
