10. A method of supplementing training for a machine-learning classifier, comprising: (a) obtaining a training set that includes training samples and corresponding assigned classification labels, the training set having been used to train a machine-learning classifier; (b) obtaining a set of unlabeled samples; (c) selecting a sample from among the training set and the set of unlabeled samples based on a specified selection criterion that involves evaluation of a plurality of both the training samples and the unlabeled samples; (d) submitting the selected sample for labeling and, in response, receiving a reply classification label for the selected sample; and (e) retraining the machine-learning classifier using the selected sample and the reply classification label for the selected sample, wherein the specified selection criterion comprises predicting labels for a group of the training samples and comparing the predicted labels to the assigned classification labels for the group of training samples.
10. A method of supplementing training for a machine-learning classifier, comprising: (a) obtaining a training set that includes training samples and corresponding assigned classification labels, the training set having been used to train a machine-learning classifier; (b) obtaining a set of unlabeled samples; (c) selecting a sample from among the training set and the set of unlabeled samples based on a specified selection criterion that involves evaluation of a plurality of both the training samples and the unlabeled samples; (d) submitting the selected sample for labeling and, in response, receiving a reply classification label for the selected sample; and (e) retraining the machine-learning classifier using the selected sample and the reply classification label for the selected sample, wherein the specified selection criterion comprises predicting labels for a group of the training samples and comparing the predicted labels to the assigned classification labels for the group of training samples. 16. A method according to claim 10 , wherein plural selected samples are selected in step (c) and submitted in step (d) for labeling as a group, arranged based on predicted labels for said selected samples.