• DocumentCode
    2767955
  • Title

    A New Topic Filter Based on Maximum Entropy Model

  • Author

    Chen, Chen ; Liu, Huilin ; Wang, Guoren ; Yu, Lili

  • Author_Institution
    Key Lab. of Med. Image Comput., Northeastern Univ., Shenyang, China
  • Volume
    7
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    495
  • Lastpage
    499
  • Abstract
    Because of the large web scale and the information requirement for special field, focuse2825453011d search has attracted more and more people. For the complexity of natural language, there are ambiguous for a word itself, and which will take some trouble for topic filter. For the two main problems, false positive and false negative, this paper proposes two new methods separately. By machine learning, we construct a guide model with the maximum entropy principle, by which we can filter the noise pages out easily and by KNN method, the false negative problem will be solved easily. The experiment shows that our model or method really out performs the base-line method.
  • Keywords
    information filters; learning (artificial intelligence); maximum entropy methods; natural languages; KNN method; base-line method; focuse2825453011d search; information requirement; machine learning; maximum entropy model; natural language; noise pages; topic filter; web scale; Biomedical imaging; Educational institutions; Entropy; Fuzzy systems; Information filtering; Information filters; Laboratories; Machine learning; Search engines; Systems engineering education;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3735-1
  • Type

    conf

  • DOI
    10.1109/FSKD.2009.709
  • Filename
    5360059