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Abstract: The graph matching task and other binary constraint satisfaction problems (CSP) can be solved by non-learning neural nets, i.e. some kind of associative memories with fixed weights. The stable states of the nets correspond to solutions of the CSP. The basic problem of this transfer of tasks to nets is to choose the parameters of the nets, for example the initial activations of the nodes, the weights and the update algorithm. Wysotzki (see [Wys90, Wys91]) describes a way of mapping a graph... (Update)
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BibTeX entry: (Update)
K. Schadler and F. Wysotzki. Theoretical foundations of a special neural net approach for graphmatching. Technical Report 96-26, TU Berlin, CS Dept., 1996. http://citeseer.ist.psu.edu/sch97theoretical.html More
@misc{ sch96theoretical,
author = "K. Sch and a Wysotzki",
title = "Theoretical foundations of a special neural net approach for graphmatching",
text = "K. Schadler and F. Wysotzki. Theoretical foundations of a special neural
net approach for graphmatching. Technical Report 96-26, TU Berlin, CS Dept.,
1996.",
year = "1996",
url = "citeseer.ist.psu.edu/sch97theoretical.html" }
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