Transfer Group Probabilities Based Learning Machine
NI Tong-guan · Dianzi xuebao · 2013
Learning from group probabilities helps to protect the privacy of users and has become a hot topic in the community of machine learning.The traditional group probabilities based learning methods have gained certain success,however,they still fall short when the prior information are not fully provided.In order to solve this problem,a novel transfer learning method called transfer group probabilities based learning machine(TGPLM in abbreviation)is proposed by integrating group probabilities into the principle of structure risk minimization.In TGPLM,a novel learning criteria is proposed based on reusing the related domain knowledge by minimizing domain similarity distance,which makes the proposed TGPLM not only make full use of the group probabilities in the current scene,but also learn the existing useful knowledge in the history scene effectively.Experimental results on the artificial,UCI and PIE face datasets show the effectiveness of the proposed method.