Lexical Acquisition Through Symbol Recirculation in Distributed Connectionist Networks

Author(s):  
Michael G. Dyer

1993 ◽  
Vol 29 (5) ◽  
pp. 827-831 ◽  
Author(s):  
Philip J. Dunham ◽  
Frances Dunham ◽  
Ann Curwin
Keyword(s):  




2001 ◽  
Vol 51 (4) ◽  
pp. 563-590 ◽  
Author(s):  
Joe Barcroft


2018 ◽  
Vol 99 ◽  
pp. 166-180 ◽  
Author(s):  
Akira Taniguchi ◽  
Tadahiro Taniguchi ◽  
Tetsunari Inamura


2002 ◽  
Vol 14 (7) ◽  
pp. 1755-1769 ◽  
Author(s):  
Robert M. French ◽  
Nick Chater

In error-driven distributed feedforward networks, new information typically interferes, sometimes severely, with previously learned information. We show how noise can be used to approximate the error surface of previously learned information. By combining this approximated error surface with the error surface associated with the new information to be learned, the network's retention of previously learned items can be improved and catastrophic interference significantly reduced. Further, we show that the noise-generated error surface is produced using only first-derivative information and without recourse to any explicit error information.



1994 ◽  
Vol 27 (4) ◽  
pp. 305-323 ◽  
Author(s):  
Steven L. Small


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