A new method for information update in supervised neural structures

Abstract Supervised neural networks can evaluate the most important features and the relations between them from a redundant set, while they allow the control on how to group the patterns. However, they do not allow an updating of the stored information without destroying the existing one. The method proposed in this paper allows a fast update of neural trees composed by supervised neural networks in their nodes; it is robust to noise and supplies good performances, in comparison with a new training, both in classification and in time required. Some tests are presented on four public domain real databases.

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