• DocumentCode
    2552202
  • Title

    Text categorization algorithms representations based on inductive learning

  • Author

    Jian-Fang, Cao ; Hong-bin, Wang

  • Author_Institution
    Dept. of Comput. Sci., Xinzhou Teachers Univ., Xinzhou, China
  • fYear
    2010
  • fDate
    16-18 April 2010
  • Firstpage
    352
  • Lastpage
    355
  • Abstract
    Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks. Categorization algorithm is the most critical factor to text categorization system performance. The inductive learning classifiers are put forward. Very accurate text categorization result can be learned automatically from training examples.
  • Keywords
    learning by example; natural language processing; pattern classification; text analysis; inductive learning classifiers; information organization; management tasks; natural language text assignment; text categorization algorithm representation; Classification tree analysis; Content management; Information filtering; Information filters; Information management; Learning systems; Machine learning; Natural languages; Testing; Text categorization; classification; inductive learnin; text categorization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-5263-7
  • Electronic_ISBN
    978-1-4244-5265-1
  • Type

    conf

  • DOI
    10.1109/ICIME.2010.5477992
  • Filename
    5477992