• DocumentCode
    2283509
  • Title

    A Learning Process Using SVMs for Multi-agents Decision Classification

  • Author

    Xiao, Yanshan ; Deng, Feiqi ; Liu, Bo ; Liu, Shouqiang ; Luo, Dan ; Liang, Guohua

  • Author_Institution
    Fac. of Inf. Technol., Univ. of Technol., Sydney, NSW
  • Volume
    3
  • fYear
    2008
  • fDate
    9-12 Dec. 2008
  • Firstpage
    583
  • Lastpage
    586
  • Abstract
    In order to resolve decision classification problem in multiple agents system, this paper first introduces the architecture of multiple agents system. It then proposes a support vector machines based assessment approach, which has the ability to learn the rules form previous assessment results from domain experts. Finally, the experiment are conducted on the artificially dataset to illustrate how the proposed works, and the results show the proposed method has effective learning ability for decision classification problems.
  • Keywords
    decision making; learning (artificial intelligence); multi-agent systems; pattern classification; support vector machines; learning process; multiagent decision classification; support vector machine; Australia; Automation; Data mining; Information technology; Intelligent agent; Management training; Multiagent systems; Risk management; Support vector machine classification; Support vector machines; multi-agent support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    978-0-7695-3496-1
  • Type

    conf

  • DOI
    10.1109/WIIAT.2008.430
  • Filename
    4740848