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International Work-Conference on Artificial Neural Networks

IWANN 1997: Biological and Artificial Computation: From Neuroscience to Technology pp 453–462Cite as

Recursive hetero-associative memories for translation

  • Plasticity Phenomena (Maturing, Learning and Memory)
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1240))

Abstract

This paper presents a modification of Pollack's RAAM (Recursive Auto-Associative Memory), called a Recursive Hetero-Associative Memory (RHAM), and shows that it is capable of learning simple translation tasks, by building a state-Space representation of each input string and unfolding it to obtain the corresponding output string. RHAM-based translators are computationally more powerful and easier to train than their corresponding double-RAAM counterparts in the literature.

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Author information

Authors and Affiliations

  1. Departament de Llenguatges i Sistemes Informàtics, Universitat d'Alacant, E-03071, Alacant, Spain

    Mikel L. Forcada & Ramón P. Ñeco

Authors
  1. Mikel L. Forcada
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  2. Ramón P. Ñeco
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Editor information

José Mira Roberto Moreno-Díaz Joan Cabestany

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© 1997 Springer-Verlag Berlin Heidelberg

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Forcada, M.L., Ñeco, R.P. (1997). Recursive hetero-associative memories for translation. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032504

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  • DOI: https://doi.org/10.1007/BFb0032504

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63047-0

  • Online ISBN: 978-3-540-69074-0

  • eBook Packages: Springer Book Archive

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