Multilayer feedforward networks are universal approximators
Neural Networks
Creating artificial neural networks that generalize
Neural Networks
Universal approximation using radial-basis-function networks
Neural Computation
Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
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
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
Note on free lunches and cross-validation
Neural Computation
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Applied Intelligence
A comparison of neural network input vector selection techniques
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial 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
A boosting approach for corporate failure prediction
Applied Intelligence
A comparison of some error estimates for neural network models
Neural Computation
Design of ensemble neural network using the Akaike information criterion
Engineering Applications of Artificial Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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
Classification of epileptic motor manifestations using inertial and magnetic sensors
Computers in Biology and Medicine
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In the domain of high-speed impact between solids, the simulation of one trial entails the use of large resources and an elevated computational cost. The objective of this research is to find the best neural network associated with a new problem of ballistic impact, maximizing the quantity of trials available and simplifying their architecture. To achieve this goal, this paper proposes a tuning performance process based on four stages. These stages include existing statistical techniques, a combination of proposals to improve the performance and analyze the influence of each variable. To measure the quality of the different networks, two criteria based on information theory have been incorporated to reflect the fit of the data with respect to their complexity. The results obtained show that the application of an integrated tuning process in this domain permits improvement in the performance and efficiency of a neural network in comparison with different machine learning alternatives