11. The medium of claim 10 , further comprising, after train the classifier and prior to apply the classifier to the additional unlabeled case, repeat at least once: apply the selection algorithm with the classifier to the augmented training set and the pool, without the per-case symmetrical importance scores, to determine new selection scores; combine the new selection scores and the corresponding per-case symmetrical importance scores to form new combined scores, each unlabeled case having an associated new combined score; provide another high scoring unlabeled case to the oracle to label; receive another labeled case back from the oracle and further augment the augmented training set with the other labeled case; and retrain the classifier with the further augmented training set.
11. The medium of claim 10 , further comprising, after train the classifier and prior to apply the classifier to the additional unlabeled case, repeat at least once: apply the selection algorithm with the classifier to the augmented training set and the pool, without the per-case symmetrical importance scores, to determine new selection scores; combine the new selection scores and the corresponding per-case symmetrical importance scores to form new combined scores, each unlabeled case having an associated new combined score; provide another high scoring unlabeled case to the oracle to label; receive another labeled case back from the oracle and further augment the augmented training set with the other labeled case; and retrain the classifier with the further augmented training set. 12. The medium of claim 11 , wherein, after the augmented training set has at least one case labeled for each class, the classifier outputs a combined score comprising: 
 w*(1−m), where w is a per-case symmetrical importance score for a case and m is a margin of the classifier's output for the case.