Generalization-Memorization Machines
This addresses the challenge of balancing memorization and generalization in machine learning, but it appears incremental as it builds on existing generalization-memorization kernel methods.
The paper tackles the problem of correctly classifying training data without overfitting by proposing generalization-memorization machines (GMM), which improve memorization abilities while avoiding overfitting, with experimental results showing effectiveness in both memorization and generalization.
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.