Abstract: Traditional machine learning methods assume that different learning tasks have nothing with each other,but in fact there are some links between them．Transfer learning attempts to use these links and even past learning experiences between different tasks to accelerate the learning for new tasks．This paper integrated the MEB (Minimum enclosing ball algorithm together with Parzen windows probability estimation to develop a new transfer learning method named MEBTL (Minimum enclosing ball Transfer learning)．We also used CVM (Core Vector Machines) theory to develop its fast version of the proposed algorithm CCMEBTL for large domain adaptation．The experimental results about “WIFI indoor positioning” and “face detection” indicate the effectiveness of the proposed algorithm．

Key words: CCMEB,Data correction,Transfer learning,Domain adaptation