24. A method according to claim 1 and also comprising: executing a plurality of learning iterations each characterized by precision and recall, only until a diminishing returns criterion is true, including: executing at least one learning iteration; computing the diminishing returns criterion; and subsequently executing at least one additional learning iteration only if the diminishing returns criterion is not true, wherein said diminishing returns criterion returns a true value if and only if a non-decreasing function of one of the precision and the recall is approaching a steady state, said non-decreasing function comprises an F-measure, and said diminishing returns criterion is computed by using a linear regression to compute a linear function estimating an F-measure obtained in previous iterations as a function of a log of a corresponding iteration number, generating a prediction of at least one F-measure at least one future iteration by finding a value along the linear function corresponding to a log of said future iteration, comparing said prediction to a currently known F-measure, and returning true if the prediction is close to said currently known F-measure to a predetermined degree.
24. A method according to claim 1 and also comprising: executing a plurality of learning iterations each characterized by precision and recall, only until a diminishing returns criterion is true, including: executing at least one learning iteration; computing the diminishing returns criterion; and subsequently executing at least one additional learning iteration only if the diminishing returns criterion is not true, wherein said diminishing returns criterion returns a true value if and only if a non-decreasing function of one of the precision and the recall is approaching a steady state, said non-decreasing function comprises an F-measure, and said diminishing returns criterion is computed by using a linear regression to compute a linear function estimating an F-measure obtained in previous iterations as a function of a log of a corresponding iteration number, generating a prediction of at least one F-measure at least one future iteration by finding a value along the linear function corresponding to a log of said future iteration, comparing said prediction to a currently known F-measure, and returning true if the prediction is close to said currently known F-measure to a predetermined degree. 25. The method according to claim 24 wherein said diminishing returns criterion returns a true value if and only if a standard deviation of the F-measure is below a threshold value.