Repeated Measures Multiple Comparison Procedures Applied to Model Selection in Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Mlps (mono layer polynomials and multi layer perceptrons) for nonlinear modeling
The Journal of Machine Learning Research
A Data Envelopment Analysis-Based Approach for Data Preprocessing
IEEE Transactions on Knowledge and Data Engineering
Gaussian fitting based FDA for chemometrics
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Predictive neuro-control of uncertain systems: design and use of a neuro-optimizer
Automatica (Journal of IFAC)
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In order to select the best predictive neural-network architecture in a set of several candidate networks, we propose a general Bayesian nonlinear regression model comparison procedure, based on the maximization of an expected utility criterion. This criterion selects the model under which the training set achieves the highest level of internal consistency, through the predictive probability distribution of each model. The density of this distribution is computed as the model posterior predictive density and is asymptotically approximated from the assumed Gaussian likelihood of the data set and the related conjugate prior density of the parameters. The use of such a conjugate prior allows the analytic calculation of the parameter posterior and predictive posterior densities, in an empirical Bayes-like approach. This Bayesian selection procedure allows us to compare general nonlinear regression models and in particular feedforward neural networks, in addition to embedded models as usual with asymptotic comparison tests