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Self-Organizing Maps in Symbol Processing

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Hybrid Neural Systems (Hybrid Neural Systems 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1778))

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Abstract

A symbol as such is disassociated from the world. In addition, as a discrete entity a symbol does not mirror all the details of the portion of the world that it is meant to refer to. Humans establish the association between the symbols and the referenced domain – the words and the world – through a long learning process in a community. This paper studies how Kohonen self-organizing maps can be used for modeling the learning process needed in order to create a conceptual space based on a relevant context with which the symbols are associated. The categories that emerge in the self-organizing process and their implicitness are considered as well as the possibilities to model contextuality, subjectivity and intersubjectivity of interpretation.

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Author information

Authors and Affiliations

  1. Media Lab, University of Art and Design, Hämeentie 135 C, FIN-00560, Helsinki, Finland

    Timo Honkela

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  1. Timo Honkela
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Editors and Affiliations

  1. University of Sunderland, SR6 0DD, Sunderland, United Kingdom

    Stefan Wermter

  2. CECS Department, University of Missouri-Colombia , 201 Engineering Building West, MO 65211-2060, Columbia, USA

    Ron Sun

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© 2000 Springer-Verlag Berlin Heidelberg

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Honkela, T. (2000). Self-Organizing Maps in Symbol Processing. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_24

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  • DOI: https://doi.org/10.1007/10719871_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67305-7

  • Online ISBN: 978-3-540-46417-4

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