Maintained by Danny Silver, Department of Computer Science, University of Western Ontario, London, Ontario N6A 5B7, Canada. These approaches do not take into account any previously learned representational information or search experience. Recently, there have been a number of efforts in a variety of areas that consider how best to capitalize on background knowledge to learn faster, or to learn more accurately with fewer examples. The following list of reference material is not meant to be complete.
Catastrophic interference is eliminated in pretrained networks.
In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society, p. 723-728. Transfer of learning in backpropagation and in related neural network models. July 1991 Pages 584-589. In C. L. Giles and S. J. Hanson and J.D. Cowan, editors, Advances in Neural Information Processing Systems 5, Morgan Kaufmann, San Mateo, CA, 1993. Non-literal transfer Among Neural Network Learners.