Improving generalization of MLPs with multi-objective optimization

This paper presents a new learning scheme for improving generalization of m ultilayer perceptrons. The algorithm uses a multi-objective optimization ap proach to balance between the error of the training data and the norm of ne twork weight vectors to avoid overfitting. The results are compared with su pport vector machines and standard backpropagation. (C) 2000 Elsevier Scien ce B.V. All rights reserved.