  The dependence on training data of the Gibbs algorithm (GA) is analytically
characterized. By adopting the expected empirical risk as the performance
metric, the sensitivity of the GA is obtained in closed form. In this case,
sensitivity is the performance difference with respect to an arbitrary
alternative algorithm. This description enables the development of explicit
expressions involving the training errors and test errors of GAs trained with
different datasets. Using these tools, dataset aggregation is studied and
different figures of merit to evaluate the generalization capabilities of GAs
are introduced. For particular sizes of such datasets and parameters of the
GAs, a connection between Jeffrey's divergence, training and test errors is
established.
