Multilayer feedforward networks are universal approximators
Neural Networks
Universal approximation using radial-basis-function networks
Neural Computation
Regularization theory and neural networks architectures
Neural Computation
Training with noise is equivalent to Tikhonov regularization
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Neural networks for pattern recognition
Neural networks for pattern recognition
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Guide to Neural Computing Applications
Guide to Neural Computing Applications
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Evolving the Topology and the Weights of Neural Networks Using a Dual Representation
Applied Intelligence
Evolutionary Learning of Modular Neural Networks withGenetic Programming
Applied Intelligence
Applied Intelligence
Approximating the Semantics of Logic Programs by Recurrent Neural Networks
Applied Intelligence
Variable Hidden Layer Sizing in Elman Recurrent Neuro-Evolution
Applied Intelligence
Neural Nets Trained by Genetic Algorithms for Collision Avoidance
Applied Intelligence
A Neural Network Based Model for Prognosis of Early Breast Cancer
Applied Intelligence
Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68)
Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68)
Prediction of the response under impact of steel armours using a multilayer perceptron
Neural Computing and Applications
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Comparison of Feature Construction Methods for Video Relevance Prediction
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
A modular neural network for super-resolution of human faces
Applied Intelligence
Learning highly non-separable Boolean functions using constructive feedforward neural network
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Fault diagnosis of nuclear power plant based on genetic-RBF neural network
International Journal of Computer Applications in Technology
Robust nonlinear system identification using neural-network models
IEEE Transactions on Neural Networks
On overfitting, generalization, and randomly expanded training sets
IEEE Transactions on Neural Networks
Collaborative Innovation for the Management of Information Technology Resources
International Journal of Human Capital and Information Technology Professionals
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In domains with limited data, such as ballistic impact, prior researches have proven that the optimization of artificial neural models is an efficient tool for improving the performance of a classifier based on MultiLayer Perceptron. In addition, this research aims to explore, in the ballistic domain, the optimization of other machine learning strategies and their application in regression problems. Therefore, this paper presents an optimization methodology to use with several approaches of machine learning in regression problems, maximizing the limited dataset and locating the best network topology and input vector of each network model. This methodology is tested in real regression scenarios of ballistic impact with different artificial neural models, obtaining substantial improvement in all the experiments. Furthermore, the quality stage, based on criteria of information theory, enables the determination of when the complexity of the network design does not penalize the fit over the data and thereby the selection of the best neural network model from a series of candidates. Finally, the results obtained show the relevance of this methodology and its application improves the performance and efficiency of multiple machine learning strategies in regression scenarios.