Hence the value of H(x) at the end of the iteration, after the labeling step, is H 2 : As we observed above, H 1 > H 0 , but if we consider the change overall, we find that the increase in the training step is more than offset in the labeling step: When one speaks of the Yarowsky algorithm, one often has in mind not just the generic algorithm Y-0 (or Y-1), but an algorithm whose specification includes the particular choice of base learning algorithm made by Yarowsky.
yarowsky's base learner constructs a decision list, that is, a list of rules of form f → j, where f is a feature and j is a label, with score θ fj .