  In this paper we propose a method for transfer learning of endoscopic images.
For transferring between features obtained from images taken by different (old
and new) endoscopes, we extend the Max-Margin Domain Transfer (MMDT) proposed
by Hoffman et al. in order to use L2 distance constraints as regularization,
called Max-Margin Domain Transfer with L2 Distance Constraints (MMDTL2).
Furthermore, we develop the dual formulation of the optimization problem in
order to reduce the computation cost. Experimental results demonstrate that the
proposed MMDTL2 outperforms MMDT for real data sets taken by different
endoscopes.
