Sequential Learning in Feedforward Networks: Proactive and Retroactive Interference Minimization

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object , Article 01 Jan 2002Publisher:Springer Berlin Heidelberg

Authors: Vicente Ruiz de Angulo; Carme Torras;

doi: 10.1007/3-540-46084-5_216

handle: 10261/30231

Sequential Learning in Feedforward Networks: Proactive and Retroactive Interference Minimization

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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.

International Conference on Artificial Neural Networks (ICANN), 2002, Madrid (España)

Peer Reviewed

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Keywords

Pattern recognition, Pattern recognition systems

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