Titlebook: Hebbian Learning and Negative Feedback Networks; Colin Fyfe Book 2005 Springer-Verlag London 2005 Artificial neural networks.Data mining.E

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The Negative Feedback NetworkComponent network which needs no weight decay in its learning rule: because of the negative feedback of activation, we can use simple Hebbian learning which will not cause instability in the weight growth process and which moreover causes the weights to converge to the Principal Components of the in

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Topology Preserving Mapsed in signal processing applications to encode a high-dimensional signal in order to minimise processing/transmission costs. The basic aim is to associate with each group of vectors of the raw data a code which uniquely identifies that group. If the vectors of the group are sufficiently alike and th

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Two Neural Networks for Canonical Correlation Analysismonstrate the network’s capabilities on artificial data and then compare its effectiveness with that of a standard statistical method on real data. We demonstrate the capabilities of the network in certain situations where standard statistical techniques are not effective, for example where we have

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