• DocumentCode
    1400589
  • Title

    A partial order for the M-of-N rule-extraction algorithm

  • Author

    Maire, Frédéric

  • Author_Institution
    Neurocomput. Res. Center, Queensland Univ. of Technol., Brisbane, Qld., Australia
  • Volume
    8
  • Issue
    6
  • fYear
    1997
  • fDate
    11/1/1997 12:00:00 AM
  • Firstpage
    1542
  • Lastpage
    1544
  • Abstract
    We present a method to unify the rules obtained by the M-of-N rule-extraction technique. The rules extracted from a perceptron by the M-of-N algorithm are in correspondence with sets of minimal Boolean vectors with respect to the classical partial order defined on vectors. Our method relies on a simple characterization of another partial order defined on Boolean vectors. We show that there exists also a correspondence between sets of minimal Boolean vectors with respect to this order and M-of-N rules equivalent to a perceptron. The gain is that fewer rules are generated with the second order. Independently, we prove that deciding whether a perceptron is symmetric with respect to two variables is NP-complete
  • Keywords
    Boolean algebra; computational complexity; knowledge acquisition; perceptrons; vectors; Boolean vectors; NP-complete; complexity; neural nets; partial order; perceptron; rule-extraction algorithm; Artificial neural networks; Australia; Neural networks;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.641475
  • Filename
    641475