Generalization theory asks the question of how well a learner classifies unseen data given only a limited amount of training data, instead of referring to the law of large numbers for guarantees in parameter estimation.
it asks how much training data is needed for an estimator to converge to a point where it has minimal error on unseen data, that is, where it is probably approximately correct.