Improving generalization of MLPs with multi-objective optimization
This paper presents a new learning scheme for improving generalization of multilayer perceptrons. The algorithm uses a multi-objective optimization approach to balance between the error of the training data and the norm of network weight vectors to avoid overfitting. The results are compared with support vector machines and standard backpropagation.
generalization multi-objective optimization learning