A Comparative Study of Methods for Transductive Transfer Learning

The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of re- search. While previous work has studied the supervised ver- sion of this problem, we study the more challenging case of unsupervised transductive transfer learning, where no la- beled data from the target domain are available at training. We describe some current state-of-the-art inductive and transductive approaches and then adapt these models to the problem of transfer learning for protein name extrac- tion. In the process, we introduce a novel maximum entropy based technique, Iterative Feature Transformation (IFT), and show that it achieves comparable performance with state-of-the-art transductive SVMs. We also show how sim- ple relaxations, such as providing additional information like the proportion of positive examples in the test data, can significantly improve the performance of some of the trans- ductive transfer learners.