• DocumentCode
    2161907
  • Title

    Author recognition by Abstract Feature Extraction

  • Author

    Yasdi, Murat ; Diri, Banu

  • Author_Institution
    Bilgisayar Muhendisligi Bolumu, Yildiz Tek. Univ., Istanbul, Turkey
  • fYear
    2012
  • fDate
    18-20 April 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The purpose of this study is to show the success of Abstract Feature Extraction Method in multi dimensional feature vectors studies. Author recognition study is taken as an application area and word root and 2 gram´s are chosen as feature vectors. The success of the Abstract Feature Extraction method in classification is shown on both Turkish and English data sets by comparing with feature extraction methods such as PCA, CFS, chi-square.
  • Keywords
    feature extraction; natural language processing; pattern classification; CFS; English data sets; PCA; Turkish data sets; abstract feature extraction method; author recognition; chi-square; multidimensional feature vectors; word root; Abstracts; Bayesian methods; Computers; Feature extraction; Niobium; Principal component analysis; Text categorization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2012 20th
  • Conference_Location
    Mugla
  • Print_ISBN
    978-1-4673-0055-1
  • Electronic_ISBN
    978-1-4673-0054-4
  • Type

    conf

  • DOI
    10.1109/SIU.2012.6204690
  • Filename
    6204690
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2161907