Given two hypothesized labelings of the test set, h 1 and h 2 , the one with the lower PAC-Bayesian bound will achieve a lower expected test risk.
one can use the bound as a principled way of choosing the parameters in the Transductive Clustering algorithm: First, a large number of different clusterings is created; then the one that achieves the lowest PAC-Bayesian bound is chosen.