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Abstract: A recently published idea is to use the A*-Algorithm to optimize the topology of Neural Networks. In this paper, optimization techniques are investigated that combine the A*-Algorithm with di erent parallel training algorithms, namely the backpropagation algorithm and several hybrid algorithms. The hybrid algorithms combine the backpropagation's steepest descent method with di erent sets of genetic operators. The di erent algorithms are compared with respect to the quality of the solution and... (Update)
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BibTeX entry: (Update)
W. Erhard, T. Fink, M. M. Gutzmann, C. Rahn, A. Doering, M. Galicki, The Improvement and Comparison of different Algorithms for Optimizing Neural Networks on the MasPar {MP}-2. Neural Computation {NC}'98, ICSC Academic Press, Ed.M. Heiss, 617-623, 1998 http://citeseer.ist.psu.edu/erhard97improvement.html More
@inproceedings{ erhard98improvement,
author = "Werner Erhard and Torsten Fink and Michael M. Gutzmann and Christoph Rahn and Axel Doering and Miroslaw Galicki",
title = "The Improvement and Comparison of different Algorithms for Optimizing Neural Networks on the MasPar {MP}-2",
booktitle = "Neural Computation -- {NC}'98",
publisher = "ICSC Academic Press",
editor = "M. Heiss",
pages = "617--623",
year = "1998",
url = "citeseer.ist.psu.edu/erhard97improvement.html" }
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