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
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© 1990 Springer-Verlag Berlin Heidelberg
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Diederich, J. (1990). Recruitment vs. Backpropagation Learning: An empirical study on re-learning in connectionist networks. In: Dorffner, G. (eds) Konnektionismus in Artificial Intelligence und Kognitionsforschung. Informatik-Fachberichte, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76070-9_20
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DOI: https://doi.org/10.1007/978-3-642-76070-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-53131-9
Online ISBN: 978-3-642-76070-9
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