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This study extends an application of efficient partition algorithm (EPA) for artificial neural network ensemble trained according to Cascade Correlation Algorithm. We show that EPA allows to decrease the number of cases in learning and validated data sets. The predictive ability of the ensemble calculated using the whole data set is not affected and in some cases it is even improved. It is shown that...
The current study investigates a method for avoidance of an overfitting/overtraining problem in Artificial Neural Network (ANN) based on a combination of two algorithms: Early Stopping and Ensemble averaging (ESE). We show that ESE provides an improvement of the prediction ability of ANN trained according to Cascade Correlation Algorithm. A simple algorithm to estimate the generalization ability of...
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