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Titlebook: Artificial Neural Networks; Learning Algorithms, N. B. Karayiannis,A. N. Venetsanopoulos Book 1993 Springer Science+Business Media New York

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https://doi.org/10.1007/978-1-4757-4547-4algorithms; convergence; high-order neural network; learning algorithm; learning scheme; neural network a
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978-1-4419-5132-8Springer Science+Business Media New York 1993
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ALADIN: Algorithms for Learning and Architecture DetermINation,ome theoretical lower and upper bounds on the sample size versus the size of a network with one layer of hidden units needed for valid generalization have been reported in (Baum and Haussier, 1989). However, there are no reliable criteria for the selection of the number of hidden units per layer req
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Performance Evaluation of Single-layered Neural Networks,redicted, due to the simplifying assumptions made in these analytical studies. The performance evaluation of optimally trained neural networks has not attracted attention proportional to their potential, probably because of the analytical difficulties involved. Nevertheless, the performance evaluati
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