This volume aims to develop the reader's understanding of the theoretical and practical issues involved in the development of efficient MLP training strategies, and to describe and evaluate the performance of a wide range of specific training algorithm. Particular emphasis is given to the development of methods which a strong theoretical foundation, rather than heuristic, "rule of thumb" training strategies. Second-Order Methods for Neural Networks will be of interest to academic researchers and postgraduate students working with neural networks (especially supervised learning with multi-layer perceptrons), industrial researchers and programmers developing neural network software, and professionals using neural networks as optimisation tools.
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