Abstract
The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors’ experiments, in some cases, argument reduction, makes the learning process harder.
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Buciak, P., Łuba, T., Niewiadomski, H., Pleban, M., Sapiecha, P., Selvaraj, H. (2003). Decomposition and Argument Reduction of Neural Networks. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_125
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_125
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