• DocumentCode
    424197
  • Title

    Incremental machine learning theorem and algorithm based on DSM method

  • Author

    Zhou, Jian-Guo ; Xia, De-Lin ; Pu, Liu-Yan ; Wu, Jing ; Jiang, Hao

  • Author_Institution
    Sch. of Electron. Inf., Wuhan Univ., Hubei, China
  • Volume
    4
  • fYear
    2004
  • fDate
    26-29 Aug. 2004
  • Firstpage
    2202
  • Abstract
    An incremental and efficient algorithm is the key to knowledge acquisition and machine learning. The DSM is defined in the paper, the important principle of the best knowledge reduction is found and a new method is put forward through analyzing the elements mud & mus in DSM. The reduction efficiency is improved and the reduced rules are the least and the number of the attributes in the rules is the least too, so it is the best knowledge reduction. We also put forward a new incremental learning method based on DSM. The method can automatically maintain the rules when the new instances are added.
  • Keywords
    knowledge engineering; learning (artificial intelligence); difference-similitude matrix; incremental machine learning theorem; knowledge acquisition; knowledge reduction; Data mining; Databases; Information systems; Knowledge acquisition; Learning systems; Machine learning; Machine learning algorithms; Mathematics; Optimization methods; Set theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
  • Print_ISBN
    0-7803-8403-2
  • Type

    conf

  • DOI
    10.1109/ICMLC.2004.1382164
  • Filename
    1382164