Breiman (2001) defines a random forest as "a classifier consisting of a collection of tree structured classifiers {h(x, Θ k ), k=1, ...} where the {Θ k } are independently identically distributed random [training] vectors and each tree casts a unit vote for   Section  # sent # words # preds # args  training  28  651  50  129  development  1.2  28  2.2  5.7  test  1.5  33  2.7  7.0   Table 1: Number of sentences, words, marked predicates, and labeled arguments in thousands the most popular class at input x."
bagging (breiman, 1996) is a form of Random Forest, where each tree is grown based on the selection, with replacement, of N random training examples, where N is the number of total examples in the training set.