  We consider the problem of selecting a subset of alternatives given noisy
evaluations of the relative strength of different alternatives. We wish to
select a k-subset (for a given k) that provides a maximum likelihood estimate
for one of several objectives, e.g., containing the strongest alternative.
Although this problem is NP-hard, we show that when the noise level is
sufficiently high, intuitive methods provide the optimal solution. We thus
generalize classical results about singling out one alternative and identifying
the hidden ranking of alternatives by strength. Extensive experiments show that
our methods perform well in practical settings.
