The problem of dealing with different distributions between training and test data is broadly investigated by the machine learning community.
approaches for dealing with domain drift are proposed within the scope of transfer learning, whose aim is to explore knowledge from one or more source tasks (henceforth, we use the terms domain and task interchangeably) and apply it to a target task (Pan and Yang, 2010).