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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ntourntoufis, P. |
| Copyright Year | 1990 |
| Abstract | Organized maps coding sensory information have been found in the brain. T. Kohenen (1988, 1989) showed that it is possible to model a self-organizing system capable of forming a reduced representation of input information into such maps. The work defines a network with identical properties but based on a RAM neuron model, namely the C-discriminator, whereas Kohonen uses the classical linear neuron model. Such RAM models have been studied before, but only within the context of a supervised training scheme, whereas in this work an unsupervised training algorithm is used. The architecture of the network is presented, and the training procedure is defined. It is shown, on a simple example, that the network behavior can be predicted considering the overlap areas between input patterns |
| Starting Page | 319 |
| Ending Page | 324 |
| File Size | 404395 |
| Page Count | 6 |
| File Format | |
| DOI | 10.1109/IJCNN.1990.137733 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1990-06-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Content Type | Text |
| Resource Type | Article |
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