• DocumentCode
    2927675
  • Title

    Based on the Reinforcement Learning Association Rules Recommendation Study

  • Author

    Wang, Jinqiao ; Yang, Qing ; Zhu, Li ; Sun, Junli

  • Author_Institution
    Dept. of Comput. Sci., Huazhong Normal Univ., Wuhan, China
  • fYear
    2009
  • fDate
    12-14 Oct. 2009
  • Firstpage
    392
  • Lastpage
    395
  • Abstract
    Reinforcement learning is an important method of machine learning. This paper using the graph theory to express varieties of knowledge points, which their´s relationship is expressed by the graph of topological graph. Applied the Technology of association rule Recommendation to deal with the relationship between these knowledge points, give the corresponding of the recommendation work flow chart. In the paper data tables used to store the knowledge points, the algorithm to demonstrate the technical of association rule Recommendation feasibility and rationality.
  • Keywords
    data mining; graph theory; learning (artificial intelligence); association rules; graph theory; machine learning; reinforcement learning; topological graph; Association rules; Computer science; Data mining; Flowcharts; Graph theory; Intelligent robots; Machine learning; Resonance light scattering; Signal generators; Testing; Recommendation Systems data mining; association rules; reinforcement learning; topological graph;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on
  • Conference_Location
    Zhuhai
  • Print_ISBN
    978-0-7695-3810-5
  • Type

    conf

  • DOI
    10.1109/SKG.2009.19
  • Filename
    5370016