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Recruitment vs. Backpropagation Learning: An empirical study on re-learning in connectionist networks

  • Joachim Diederich
Conference paper
Part of the Informatik-Fachberichte book series (INFORMATIK, volume 252)

Abstract

This paper describes a first comparison between two connectionist learning techniques: backpropagation and recruitment learning. The task is to re-learn a conceptual representation, i.e. to significantly change a representation in an additional training period by the use of new data. Backpropagation denotes to a widely known, supervised learning technique which requires the repeated presentation of a set of training instances. Recruitment learning denotes to a technique which converts network units from a pool of free units into units which carry meaningful information, and can be used for both, instruction-based and similarity-based learning. It will be shown that a learning technique which makes use of structured knowledge (i.e. recruitment learning), re-learns and modifies a connectionist representation faster than backpropagation.

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References

  1. Diederich, J. (1989): Instruction and High-Level Learning in Connectionist Networks. Connection Science. Vol.1, No.2, 161–180.CrossRefGoogle Scholar
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  3. Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986): Learning Internal Representations by Error Propagation. In: Rumelhart, D.E. & McClelland, J.L. (Eds.): Parallel Distributed Processing. Vol 1.: Foundations. The MIT Press, Cambridge, Mass.Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Joachim Diederich
    • 1
    • 2
  1. 1.German National Research Center for Computer Science (GMD)St. AugustinGermany
  2. 2.University of California, DavisDavisUSA

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