Our work focuses on inductive transfer learning, a setting in which one assumes that both source and target tasks share the same features and label spaces. We demonstrate that transfer learning can be successfully used for feature reduction and hence for more efficient classification performance. Further, our experiments show that this approach increases the precision of the classification task as well.
