ICML 2011 workshop on unsupervised and transfer learning

Transfer Learning in Computational Biology
Christian Widmer and Gunnar Raetsch
MPI, Germany

Computational Biology provides a wide range of applications for Transfer Learning methods. As the generation of labels often is very costly in the biomedical domain, combining data from different related problems or tasks is a promising strategy to reduce label cost. In this paper, we present two problems from sequence biology, where transfer learning has been successfully applied. For this, we use regularization based Transfer Learning methods, with a special focus on the case of a hierarchical relationship between tasks. Furthermore, we propose strategies to refine the measure of task relatedness, which is of central importance in Transfer Learning and finally give some practical guidelines, when MTL strategies are likely to work.