1. A method to produce in a computer system a bound on the error of a classifier over a set of working examples, the method comprising: assigning multiple possible output assignments of values to examples in a working set; applying a filter to the assignments to determine likely assignments, by computing a ranking of the training set and working set among some possible ways to partition the combined training set and working set into a possible training set and a possible working set; and storing the error of a maximum-error likely assignment.