http://www.cs.cmu.edu/~wcohen/slipper/
 "One disadvantage of boosting is that improvements in accuracy are often obtained at the expense of comprehensibility.
 SLIPPER = simple learner with iterative pruning to produce error reduction.
 Inner loop: the weak lerner splits the training data, grows a single rule using one subset of the data, and then prunes the rule using the other subset.
 They use a confidencerated prediction based boosting algorithm, which allows the algorithm to abstain from examples not covered by the rule.
 the sign of h(x)  the weak learner's hyposthesis  is interpreted as the predited label and the magnitude h(x) is the confidence in the prediction.
 SLIPPER only handles twoclass problems now, but can be extended..
 Is better than, though not dramatically so, than c5rules (a commercial version of Quinlan's decision tree algorithms).
 see also the excellent overview at http://www.cs.princeton.edu/~schapire/uncompresspapers.cgi/msri.ps
