3. The method of claim 1 , wherein the training set is initially empty or has a small number of labeled cases, and the processor ignores the selection scores when combining the selection scores and the corresponding per-case symmetrical importance scores until the training set is viable.
3. The method of claim 1 , wherein the training set is initially empty or has a small number of labeled cases, and the processor ignores the selection scores when combining the selection scores and the corresponding per-case symmetrical importance scores until the training set is viable. 4. The method of claim 3 , further comprising, after training the classifier and prior to applying the classifier to the additional unlabeled case, repeating at least once: applying 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; combining 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; providing another high scoring unlabeled case to the oracle to label; receiving another labeled case back from the oracle and further augmenting the augmented training set with the other labeled case; and retraining the classifier with the further augmented training set.