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Multilayer feedforward networks are universal approximators
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Adaptation in natural and artificial systems
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Iterative solution of nonlinear equations in several variables
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Genetic Algorithms in Search, Optimization and Machine Learning
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Genetic Algorithms and Simulated Annealing
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Methods for Solving Systems of Nonlinear Equations
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Entropic Measures with Radial Basis Units
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Simulation-Based Risk Reduction for Planning Inspections
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Computational Intelligence: Concepts to Implementations
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Intelligent Data Analysis
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Empirical determination of sample sizes for multi-layer perceptrons by simple RBF networks
WSEAS Transactions on Computers
Bayesian decision theory on three-layer neural networks
Neurocomputing
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ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Detecting evolutionary financial statement fraud
Decision Support Systems
Fluctuations in Economic and Activity and Stabilization Policies in the CIS
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ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
The effect of training set size for the performance of neural networks of classification
WSEAS Transactions on Computers
Bayesian learning of neural networks adapted to changes of prior probabilities
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A new expert system for pediatric respiratory diseases by using neural networks
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
Discriminant analysis by a neural network with mahalanobis distance
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A new algorithm for learning mahalanobis discriminant functions by a neural network
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Stochastic perturbation methods for spike-timing-dependent plasticity
Neural Computation
Bayesian forecaster using class-based optimization
Applied Intelligence
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
Evaluating direction-of-change forecasting: Neurofuzzy models vs. neural networks
Mathematical and Computer Modelling: An International Journal
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FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds
Computers in Biology and Medicine
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The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.