Decisior Implementation in Neural Model Selection by Multi-objective Optimization

This work presents a new learning scheme for improving generalization of Multilayer Perceptrons (MLPs). The proposed Multi-objective algorithm (MOBJ) approach minimizes both the sum of squared error and the norm of network weight vectors to obtain the Pareto-optimal solutions [1]. Preliminar results are shown in [3]. Since the Pareto-optimal solutions are not unique, we need a decision phase in order to choose the best one as a final solution by using a validation set. The final solution is expected to balance network variance and bias [2] and, as a result, generates a solution with high generalization capacity, avoiding over and underfitting.