Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Multilayer feedforward networks are universal approximators
Neural Networks
Machine Learning
Voting over Multiple Condensed Nearest Neighbors
Artificial Intelligence Review - Special issue on lazy learning
Prototype selection for composite nearest neighbor classifiers
Prototype selection for composite nearest neighbor classifiers
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Ensemble of Linear Perceptrons with Confidence Level Output
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Class-Dependant Resampling for Medical Applications
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Adaptive mixtures of local experts
Neural Computation
Negative correlation learning and the ambiguity family of ensemble methods
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
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We here compare the performance (predictive accuracy and processing time) of different neural network ensembles with that of nearest neighbor classifier ensembles. Concerning the connectionist models, the multilayer perceptron and the modular neural network are employed. Experiments on several real-problem data sets demonstrate a certain superiority of the nearest-neighbor-based schemes, in terms of both accuracy and computing time. When comparing the neural network ensembles, one can observe a better behavior of the multilayer perceptron than that of the modular networks.