• DocumentCode
    2703033
  • Title

    Morphological rules of similarity for hierarchical distributed representations

  • Author

    de L. Pereira Castro, J.

  • Author_Institution
    Programa de Comput. Cientifica, Fundacao Oswaldo Cruz., Rio de Janeiro
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    267
  • Lastpage
    272
  • Abstract
    This paper presents and discusses four new criteria that are able to correctly identify hierarchical relationships (in top-down and bottom-up fashion) created upon binary distributed representations. Each criteria is mathematically formulated in terms of two separate rules of similarity, each one being able to identify one type of hierarchical relationship. The rules are also presented in terms of a given frame of reference in accordance with the hierarchical distributed representations. Several mathematical correspondences are shown among different criteria and the rules that compose them. It is proven that the new rules used to identify hierarchical relationships among two patterns represent the mathematical decomposition of the hamming distance among them. The combination of the rules able to identify one type of hierarchical relationship can be used to identify particular cluster of patterns with special meaning. This diminishes the need of defining state spaces with high-dimensionality
  • Keywords
    content-addressable storage; finite state machines; neural nets; pattern recognition; state-space methods; associative memory; binary distributed representations; hamming distance; morphological similarity rules; neural nets; neural state machines; pattern recognition; state spaces; Hamming distance; State-space methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
  • Conference_Location
    Rio de Janeiro, RJ
  • ISSN
    1522-4899
  • Print_ISBN
    0-7695-0856-1
  • Type

    conf

  • DOI
    10.1109/SBRN.2000.889750
  • Filename
    889750