  Suppose some classifiers are selected from a set of hypothesis classifiers to
form an equally-weighted ensemble that selects a member classifier at random
for each input example. Then the ensemble has an error bound consisting of the
average error bound for the member classifiers, a term for selectivity that
varies from zero (if all hypothesis classifiers are selected) to a standard
uniform error bound (if only a single classifier is selected), and small
constants. There is no penalty for using a richer hypothesis set if the same
fraction of the hypothesis classifiers are selected for the ensemble.
