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Recursive hetero-associative memories for translation

  • Mikel L. Forcada
  • Ramón P. Ñeco
Plasticity Phenomena (Maturing, Learning and Memory)
Part of the Lecture Notes in Computer Science book series (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.

Keywords

Hide Neuron Recurrent Neural Network Hide Unit Input String Empty String 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Mikel L. Forcada
    • 1
  • Ramón P. Ñeco
    • 1
  1. 1.Departament de Llenguatges i Sistemes InformàticsUniversitat d'AlacantAlacantSpain

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