• DocumentCode
    178610
  • Title

    VC-Dimension of Rule Sets

  • Author

    Yildiz, O.T.

  • Author_Institution
    Dept. of Comput. Eng., Isik Univ., Istanbul, Turkey
  • fYear
    2014
  • fDate
    24-28 Aug. 2014
  • Firstpage
    3576
  • Lastpage
    3581
  • Abstract
    In this paper, we give and prove lower bounds of the VC-dimension of the rule set hypothesis class where the input features are binary or continuous. The VC-dimension of the rule set depends on the VC-dimension values of its rules and the number of inputs.
  • Keywords
    learning (artificial intelligence); set theory; VC-dimension values; binary input features; continuous input features; lower bounds; rule set hypothesis class; Computers; Decision trees; Labeling; Pattern recognition; Statistical learning; Training; Vectors; Rule sets; VC-Dimension;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2014 22nd International Conference on
  • Conference_Location
    Stockholm
  • ISSN
    1051-4651
  • Type

    conf

  • DOI
    10.1109/ICPR.2014.615
  • Filename
    6977327
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=178610