When the target tasks are treated as different (but related), the label distribution p(L|X) is dependent on the domain.
given a domain d, the problem can be formalized as modeling p(L|X) d based on X d , p(X) d and a set of examples: 8 In the context of domain adaptation, there is a target domain t and a source domain s, with labeled data available for both, and the goal is to induce a more accurate target domain model p(L|X) t from {x i,t , l i } ∪ {x i,s , l i } than the one learned from {x i,t , l i } only.