Bagging or bootstrap aggregation is a special case of model averaging. Given a standard training set bagging generates $m$ new training sets by bootstrapping, and then the results of using some training method on the $m$ generated data sets are averaged. Bagging can stabilize results from some unstable methods such as trees.