  Transfer learning assumes classifiers of similar tasks share certain
parameter structures. Unfortunately, modern classifiers uses sophisticated
feature representations with huge parameter spaces which lead to costly
transfer. Under the impression that changes from one classifier to another
should be ``simple'', an efficient transfer learning criteria that only learns
the ``differences'' is proposed in this paper. We train a \emph{posterior
ratio} which turns out to minimizes the upper-bound of the target learning
risk. The model of posterior ratio does not have to share the same parameter
space with the source classifier at all so it can be easily modelled and
efficiently trained. The resulting classifier therefore is obtained by simply
multiplying the existing probabilistic-classifier with the learned posterior
ratio.
