• DocumentCode
    2305744
  • Title

    The Key Theorem of Statistical Learning Theory with Rough Samples

  • Author

    Liu, Yang ; Dong, Kai-kun ; Guo, Li ; Yuan, Xing-Ling

  • Author_Institution
    Harbin Inst. of Technol. at Weihai, Weihai, China
  • Volume
    4
  • fYear
    2009
  • fDate
    19-21 May 2009
  • Firstpage
    543
  • Lastpage
    547
  • Abstract
    A key theorem of statistical learning theory with rough samples is proposed. The theorem provides a theoretical basis for the applied research of supporting vector machine etc. and therefore plays an important role in statistical learning theory. In view of the uncertainty of the real world, this paper combines the trust theory and statistical learning theory to generalize the key theorem of learning theory. Random samples are replaced with rough samples and rough empirical risk minimization principle is proposed. The theorem is proven in detail.
  • Keywords
    learning (artificial intelligence); statistical analysis; support vector machines; key theorem; rough empirical risk minimization principle; rough samples; statistical learning theory; supporting vector machine; trust theory; Machine learning; Mathematics; Pattern recognition; Risk management; Software engineering; Statistical learning; Statistics; Support vector machines; Turning; Uncertainty; Trust theory; rough empirical risk minimization principle; the key theorem;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering, 2009. WCSE '09. WRI World Congress on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-0-7695-3570-8
  • Type

    conf

  • DOI
    10.1109/WCSE.2009.23
  • Filename
    5319619