21. The system according to claim 4 wherein said relevance rater comprises an electronic document analyzer receiving N electronic documents pertaining to a case encompassing a set of issues including at least one issue and establishing relevance of at least the N documents to at least one individual issue in the set of issues, the analyzer comprising: iterative binary relevance determining apparatus for iteratively generating binary relevance data characterizing relevance of said documents to at least one issue in said set of issues by performing machine learning on an iteration-specific training set of documents, said binary relevance data being generated by iteratively computing a cut-off point, wherein for at least one individual iteration, said iteration-specific training set is well distributed about the cut-off point as computed in said individual iteration.
22. The system according to claim 21 wherein for each individual iteration, said iteration-specific training set is well distributed about the cut-off point as computed in said individual iteration.