Abstract
There is investigated the problem of structuring neural networks. This is a crucial task in designing neural networks similar to the corresponding task of designing modular software systems. We propose a particulary new approach of invoking inductive inference techniques for recognizing regularities in the partially given relation to be implemented and for proposing a prestructured net. Furthermore there will be given some mathematical foundations of the choosen approach in form of some convergence theorems by using the back-propagation strategy. Finally we show some connections to the field of PAC-Learning.
This work has been supported by the German Ministry for Research and Technology (BMFT) under grant no. 01 IW 101.
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© 1995 Springer-Verlag Berlin Heidelberg
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Pippig, E. (1995). Structuring neural networks and PAC-Learning. In: Jantke, K.P., Lange, S. (eds) Algorithmic Learning for Knowledge-Based Systems. Lecture Notes in Computer Science, vol 961. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60217-8_20
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DOI: https://doi.org/10.1007/3-540-60217-8_20
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