The implementation of neural network for semiconductor PECVD process
Expert Systems with Applications: An International Journal
A neural-network approach for an automatic LED inspection system
Expert Systems with Applications: An International Journal
A three-stage integrated approach for assembly sequence planning using neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Letters: Prediction error of a fault tolerant neural network
Neurocomputing
Process parameter optimization for MIMO plastic injection molding via soft computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Artificial neural network for the joint modelling of discrete cause-specific hazards
Artificial Intelligence in Medicine
Improving weighted information criterion by using optimization
Journal of Computational and Applied Mathematics
A new architecture selection method based on tabu search for artificial neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Learning in a higher-order simple perceptron
Mathematical and Computer Modelling: An International Journal
Linear support vector machines via dual cached loops
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary artificial neural networks: a review
Artificial Intelligence Review
International Journal of Productivity Management and Assessment Technologies
Computer Methods and Programs in Biomedicine
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The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set