Fast learning algorithms for neural networks

A generalized criterion for the training of feedforward neural networks is proposed. Depending on the optimization strategy used, this criterion leads to a variety of fast learning algorithms for single-layered as well as multilayered neural networks. The simplest algorithm devised on the basis of this generalized criterion is the fast delta rule algorithm, proposed for the training of single-layered neural networks. The application of a similar optimization strategy to multilayered neural networks in conjunction with the proposed generalized criterion provides the fast backpropagation algorithm. Another set of fast algorithms with better convergence properties is derived on the basis of the same strategy that provided recently a family of Efficient LEarning Algorithms for Neural NEtworks (ELEANNE). Several experiments verify that the fast algorithms developed perform the training of neural networks faster than the corresponding learning algorithms existing in the literature.<
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