Abstract
Multi-nets promise an improved performance over monolithic neural networks by virtue of their distributed implementation. This potential lacks popularity as, without precautions, the learning rate has to drop considerably to eliminate the occurrence of unlearning. This paper introduces extensions of the Error Back-Propagation algorithm to enable function preserving merging of neural modules at full learning rate.
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Venema, R.S., Spaanenburg, L. (2001). Learning Feed-Forward Multi-Nets. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_24
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_24
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
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