Abstract
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.
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This partitioning of the available data into a small training set and a large cross-validation set is not realistic for practical applications. The small training set size was chosen for testing the effects of overfitting. The large cross-validation set was used for getting a reliable estimate of the weighting scheme (13.33), with which the alternative weighting scheme (13.31) and a uniform weighting scheme are to be compared.
Values that had achieved good results in the simulations of Chapter 16 were simply used again.
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© 1999 Springer-Verlag London Limited
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Husmeier, D. (1999). Demonstration: Committees of Networks Trained with Different Regularisation Schemes. In: Neural Networks for Conditional Probability Estimation. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0847-4_14
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DOI: https://doi.org/10.1007/978-1-4471-0847-4_14
Publisher Name: Springer, London
Print ISBN: 978-1-85233-095-8
Online ISBN: 978-1-4471-0847-4
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