1. A method for multi-label active learning, the method comprising: accepting a set of training samples, the set of training samples including multiple respective samples that are each respectively associated with multiple labels; analyzing the set of training samples to select a sample-label pair responsive to at least one error parameter; and submitting the selected sample-label pair to an oracle for labeling.