  In this paper we propose an improved method for transfer learning that takes
into account the balance between target and source data. This method builds on
the state-of-the-art Multisource Tradaboost, but weighs the importance of each
datapoint taking into account the amount of target and source data available. A
comparative study is then presented exposing the performance of four transfer
learning methods as well as the proposed Weighted Multisource Tradaboost. The
experimental results show that the proposed method is able to outperform the
base method as the number of target samples increase. These results are
promising in the sense that source-target ratio weighing may be a path to
improve current methods of transfer learning. However, against the asymptotic
conjecture, all transfer learning methods tested in this work get outperformed
by a no-transfer SVM for large number on target samples.
