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NeuroCOLT
Technical Report NC-TR-95-024
Graphs
and Artificial Neural Networks
Martin
Anthony
London School of Economics and Political Science
University of London
Abstract
`Artificial neural networks' are machines (or models of computation)
based loosely on the ways in which the brain is believed to work.
In this chapter, we discuss some links between graph theory and artificial
neural networks. We describe how some combinatorial optimisation tasks
may be approached by using a type of artificial neural network known
as a Boltzmann machine. We then focus on `learning' in feedforward
artificial neural networks, explaining how the graph structure of
a network and the hardness of graph-colouring quantify the complexity
of learning.
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Postscript
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