  Classifying the training data correctly without over-fitting is one of the
goals in machine learning. In this paper, we propose a
generalization-memorization mechanism, including a generalization-memorization
decision and a memory modeling principle. Under this mechanism, error-based
learning machines improve their memorization abilities of training data without
over-fitting. Specifically, the generalization-memorization machines (GMM) are
proposed by applying this mechanism. The optimization problems in GMM are
quadratic programming problems and could be solved efficiently. It should be
noted that the recently proposed generalization-memorization kernel and the
corresponding support vector machines are the special cases of our GMM.
Experimental results show the effectiveness of the proposed GMM both on
memorization and generalization.
