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Artificial Neural Nets and Genetic Algorithms pp 106–109Cite as

Unlearning in Feed-Forward Multi-Nets

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Abstract

Multi-nets promise an improved performance over monolithic neural networks by virtue of their distributed implementation. Modular neural networks are multi-nets based on an judicious assembly of functionally different parts. This can be viewed as again a monolithic network, but with more complex neurons (the neural modules). Therefore they will share the same learning problems, notably the unlearning effect. In this paper we will look more closely into the reasons for unlearning and discuss how this can be applied to detect novelties.

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Author information

Authors and Affiliations

  1. Rijksuniversiteit Groningen, Dept. of Mathematics and Computing Science, P.O. Box 800, NL-9700 AV, Groningen, The Netherlands

    L. Spaanenburg

Authors
  1. L. Spaanenburg
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Czech Republic

    Věra Kůrková  & Roman Neruda  & 

  2. Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Prague, Czech Republic

    Miroslav Kárný

  3. Division of Mathematics, School of Mathematical and Information Sciences Coventry University, Coventry, UK

    Nigel C. Steele

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© 2001 Springer-Verlag Wien

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Spaanenburg, L. (2001). Unlearning in Feed-Forward Multi-Nets. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_25

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  • DOI: https://doi.org/10.1007/978-3-7091-6230-9_25

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83651-4

  • Online ISBN: 978-3-7091-6230-9

  • eBook Packages: Springer Book Archive

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