Abstract
We tackle the catastrophic interference problem with a formal approach. The problem is divided into two subproblems. The first arises when one tries to introduce some new information in a previously trained network, without distorting the stored information. The second is how to encode a set of patterns so as to preserve them when new information has to be stored. We suggest solutions to both subproblems without using local representations or retraining.
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© 2002 Springer-Verlag Berlin Heidelberg
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Ruiz de Angulo, V., Torras, C. (2002). Sequential Learning in Feedforward Networks: Proactive and Retroactive Interference Minimization. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_216
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DOI: https://doi.org/10.1007/3-540-46084-5_216
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