The cascade-correlation learning architecture
Advances in neural information processing systems 2
On cross validation for model selection
Neural Computation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving generalization of constructive neural networks using ensembles
Improving generalization of constructive neural networks using ensembles
Asymptotic statistical theory of overtraining and cross-validation
IEEE Transactions on Neural Networks
Cross-validation with active pattern selection for neural-network classifiers
IEEE Transactions on Neural Networks
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This research investigates cross-validation techniques for performing neural network ensemble generation and performance evaluation. The chosen framework is the Neural Network Ensemble Simulator (NNES). Ensembles of classifiers are generated using level-one cross-validation. Extensive modeling is performed and evaluated using level-two cross-validation. NNES 4.0 automatically generates unique data sets for each student and each ensemble within a model. The results of this study confirm that level-one cross-validation improves ensemble model generation. Results also demonstrate the value of level-two cross-validation as a mechanism for measuring the true performance of a given model.