43. The method according to claim 42 and wherein said process also comprises: partitioning said at least N documents into uniformly ranked subsets of documents, said uniformly ranked subsets differing in ranking of their member documents by said text classifier and adding more documents from each of said uniformly ranked subsets to said training subset; selecting a cut-off point for binarizing said rankings of said documents in said control subset; using said cut-off point, computing and storing at least one quality criterion characterizing said binarizing of said rankings of said documents in said control subset, thereby to define a quality of performance indication of a current iteration I; displaying a comparison of the quality of performance indication of the current iteration I to quality of performance indications of previous iterations; seeking an input as to whether or not to return to said receiving step thereby to initiate a new iteration I+1 which comprises said receiving, building, running, partitioning, selecting, and computing/storing steps and initiating said new iteration I+1 if and only if so indicated by said input; and running the text classifier most recently built on at least said N documents thereby to generate a final output and generating a computer display of said final output.
43. The method according to claim 42 and wherein said process also comprises: partitioning said at least N documents into uniformly ranked subsets of documents, said uniformly ranked subsets differing in ranking of their member documents by said text classifier and adding more documents from each of said uniformly ranked subsets to said training subset; selecting a cut-off point for binarizing said rankings of said documents in said control subset; using said cut-off point, computing and storing at least one quality criterion characterizing said binarizing of said rankings of said documents in said control subset, thereby to define a quality of performance indication of a current iteration I; displaying a comparison of the quality of performance indication of the current iteration I to quality of performance indications of previous iterations; seeking an input as to whether or not to return to said receiving step thereby to initiate a new iteration I+1 which comprises said receiving, building, running, partitioning, selecting, and computing/storing steps and initiating said new iteration I+1 if and only if so indicated by said input; and running the text classifier most recently built on at least said N documents thereby to generate a final output and generating a computer display of said final output. 44. The method according to claim 43 wherein said cut-off point is selected from all ranks of documents in said control subset so as to maximize a quality criterion.