• DocumentCode
    2198981
  • Title

    Bayesian on-line learning: a sequential Monte Carlo with Rao-Blackwellization

  • Author

    Yosui, K. ; Kurihara, T. ; Wada, K. ; Souma, T. ; Matsumoto, T.

  • Author_Institution
    Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    99
  • Lastpage
    108
  • Abstract
    This paper proposes a Rao-Blackwellised sequential Monte Carlo (RBSMC) scheme for on-line learning with feedforward neural nets. The proposed algorithm is tested against an example and the performance is compared with those of the conventional sequential Monte Carlo as well as the extended Kalman filter (EKF). The proposed scheme outperforms those conventional algorithms.
  • Keywords
    Monte Carlo methods; belief networks; feedforward neural nets; learning (artificial intelligence); Bayesian on-line learning; RBSMC scheme; Rao-Blackwellised sequential Monte Carlo scheme; feedforward neural nets; on-line learning; performance; Bayesian methods; Feeds; Kalman filters; Monte Carlo methods; Neural networks; Nonlinear filters; Sampling methods; Sequential analysis; Training data; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
  • Print_ISBN
    0-7803-7616-1
  • Type

    conf

  • DOI
    10.1109/NNSP.2002.1030021
  • Filename
    1030021
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2198981