• DocumentCode
    446035
  • Title

    Third-order generalization and a new approach to systematically categorizing higher-order generalization

  • Author

    Neville, Richard S.

  • Author_Institution
    Sch. of Informatics, Manchester Univ., UK
  • Volume
    3
  • fYear
    2005
  • fDate
    31 July-4 Aug. 2005
  • Firstpage
    1924
  • Abstract
    Higher-order generalization is a means of categorizing different types of generalization. The paper presents a framework within which higher-order generalization can be evaluated in a detailed and systematic way. Previous research divided generalization into three categories. However, these categories were fuzzy and imprecise. This paper further refines existing definitions by first assigning each category a logical predicate that it must fulfil in order to achieve a specific order (type) of generalization. Then, it breaks the orders down into four different categories in a detailed and systematic way. The paper focuses on early (initial) results; some of the aims have been demonstrated and amplified through the experimental work.
  • Keywords
    generalisation (artificial intelligence); higher-order generalization; logical predicate; third-order generalization; Artificial neural networks; Equations; Informatics; Network topology; Neurons; Optimization methods; Phase estimation; Probability distribution; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
  • Print_ISBN
    0-7803-9048-2
  • Type

    conf

  • DOI
    10.1109/IJCNN.2005.1556174
  • Filename
    1556174