32. The method according to claim 31 wherein said setting said criterion to true comprises: computing first and second values of the linear function at, respectively, a first point corresponding to a number of iterations already performed and a second point corresponding to a number of iterations more than all of which were already performed, and setting the diminishing returns criterion to true if the difference between said first and second values is pre-determinedly small.
31. 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, and said executing the plurality of learning iterations comprises: computing a linear function estimating a logarithmic transformation of an F-measure; and setting said criterion to true if the linear function is approaching a steady state.