• DocumentCode
    3146591
  • Title

    A Talent Classification Method Based on SVM

  • Author

    Hu, Hua ; Ye, Jing ; Chai, Chunlai

  • Author_Institution
    Zhejiang Gongshang Univ., Hangzhou, China
  • fYear
    2009
  • fDate
    15-16 May 2009
  • Firstpage
    160
  • Lastpage
    163
  • Abstract
    Nowadays, any employment and recruitment Web sites receive immense personal information and recruit information every day. But most information canpsilat be properly analyzed and canpsilat meet the recruit requirement. In fact, the recruiting units are looking for talents of both high and low levels talents. However, many talents information canpsilat be evaluated correctly so that the appliers lose their job opportunities. This paper will research that a non-linear quadratic classification method applies in the personnel data from a job site. The method is support vector machine based on radial basis function support. According to this classification method classifying the sample data, we have got more satisfactory results than by another classification method such as decision tree.
  • Keywords
    Web sites; classification; job specification; radial basis function networks; recruitment; support vector machines; SVM; job site; nonlinear quadratic classification method; personal information; radial basis function; recruitment Web site; support vector machine; talent classification method; Bayesian methods; Classification tree analysis; Computer science; Decision trees; Employment; Humans; Personnel; Recruitment; Support vector machine classification; Support vector machines; radial basis function; support vector machine; talent classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Ubiquitous Computing and Education, 2009 International Symposium on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-3619-4
  • Type

    conf

  • DOI
    10.1109/IUCE.2009.63
  • Filename
    5223259