Neural networks and the bias/variance dilemma
Neural Computation
Neural networks for pattern recognition
Neural networks for pattern recognition
Machine Learning
Bias/variance decompositions for likelihood-based estimators
Neural Computation
Swarm intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Variance and Bias for General Loss Functions
Machine Learning
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
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In the search of a system satisfying all the requirements of an automated classifier, evolutionary artificial neural networks have been successfully applied to a number of problems, in particular to problems where there is not a deep knowledge of the phenomena and other methods tend to fail. There are many neural models that efficiently solve either function approximation problems in general terms or some particular problems like classification, pattern recognition, clustering and time-series prediction. This success is due to these models main characteristics, in particular those matching the essentials for an automated classifier: keeping bias and variance low. This paper presents a classifier system based on the benefits arising from the interaction between evolutionary algorithms, such as particle swarm optimization, and artificial neural networks. And a bias variance decomposition of the predictive error shows that the success of the proposed approach lies in the ability of the learning algorithm to properly tune the bias/variance trade-off to reduce the prediction error. To measure the performance, the porposed classifier will be tested on three different well known benchmark problems: the Fisher Iris data set, the Australian credit card assessment and the Pima diabetes data set.