The general idea is to exploit the pairwise preferences induced from the data by training on pairs of patterns, rather than independently on each pattern.
given a weight vector α, the score for a pattern x (a candidate answer) is given by the inner product between the pattern and the weight vector: the error function depends on pairwise scores.