Bayesian regularization and pruning using a Laplace prior
Neural Computation
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Using neural networks to model conditional multivariate densities
Neural Computation
Hourly ozone prediction for a 24-h horizon using neural networks
Environmental Modelling & Software
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Prediction of episodes of poor air quality using artificial neural networks is investigated. Logistic regression,con ventional sumof-squares regression and heteroscedastic sum-of-squares regression are employed for the task of predicting real-life episodes of poor air quality in urban Belfast due to SO2. In each case,a Bayesian regularisation scheme is used to prevent over-fitting of the training data and to provide pruning of redundant model parameters. Non-linear models assuming a heteroscedastic Gaussian noise process are shown to provide the best predictors of pollutant concentration of the methods investigated.