  The paper proves generalization results for a class of stochastic learning
algorithms. The method applies whenever the algorithm generates an absolutely
continuous distribution relative to some a-priori measure and the Radon Nikodym
derivative has subgaussian concentration. Applications are bounds for the Gibbs
algorithm and randomizations of stable deterministic algorithms as well as
PAC-Bayesian bounds with data-dependent priors.
