Demonstration: Committees of Networks Trained with Different Regularisation Schemes

An ensemble of GM-RVFL networks is applied to the stochastic time series generated from the logistic-kappa map, and the dependence of the generalisation performance on the regularisation method and the weighting scheme is studied. For a single-model predictor, application of the Bayesian evidence scheme is found to lead to superior results. However, when using network committees, under-regularisation can be advantageous, since it leads to a larger model diversity, as a result of which a more substantial decrease of the generalisation ‘error’ can be achieved.