Multilayer feedforward networks are universal approximators
Neural Networks
Approximation capabilities of multilayer feedforward networks
Neural Networks
Forecasting with neural networks
Information and Management
Neural networks: applications in industry, business and science
Communications of the ACM
A classification approach using multi-layered neural networks
Decision Support Systems - Special issue on neural networks for decision support
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Artificial neural networks and their business applications
Information and Management
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
Neural network models for a resource allocation problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Neural networks have been repeatedly shown to outperform traditional statistical modeling techniques for both discriminant analysis and forecasting. While questions regarding the effects of architecture, input variable selection, learning algorithm, and size of training sets on the neural network model's performance have been addressed, very little attention has been focused on distribution effects of training and out-of-sample populations on neural network performance. This article examines the effect of changing the population distribution within training sets for estimated distribution density functions, in particular for a credit risk assessment problem.