Active learning for reducing bias and variance of a classifier using Jensen-Shannon divergence

We consider reducing loss of a classifier by decreasing its bias and variance. Embarking upon classification of scarcely labeled data, we use active learning approach in semi-supervised learning, and show that we can speed up convergence to a desired level of loss. Our focus, in this paper, is on the best instance selection for labeling the unlabeled data; we use Jensen-Shannon divergence as one selection criterion. We show that our single instance selection approaches are superior to multiple selection approach. Empirical results indicate that this method can decrease classification loss significantly.