Architecture-Independent Approximation of Functions
Neural Computation
A framework to deal with interference in connectionist systems
AI Communications
On-line learning with minimal degradation in feedforward networks
IEEE Transactions on Neural Networks
Natural inspiration for artificial adaptivity: some neurocomputing experiences in robotics
UC'05 Proceedings of the 4th international conference on Unconventional Computation
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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.