1. A method of machine learning, comprising: (a) obtaining a training set that includes training samples and corresponding assigned classification labels; (b) training an automated classifier against the training set; (c) selecting at least one of the training samples; (d) requesting confirmation/re-labeling of said at least one training sample; (e) in response to step (d), receiving a reply classification label for said at least one training sample; (f) retraining the automated classifier using the reply classification label; and, predicting a label for said at least one training sample, wherein said at least one training sample is selected in step (c) based on a comparison between the assigned classification label and the predicted label for said at least one training sample.
9. A method according to claim 1 , wherein said at least one training sample is selected based on at least one of: (i) an estimation of an effect of changing the assigned classification label for said at least one training sample; (ii) a result of a cross- validation calculation; (iii) a score representing a fit of the at least one training sample to a model based on the training set; (iv) an active-learning calculation; (v) a consistency evaluation; (vi) a prediction-confidence evaluation; (vii) a variety evaluation; and (viii) a calculated measure indicating confidence in the assigned classification label for said at least one training sample.