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
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;
Conference_Titel :
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
Print_ISBN :
0-7803-7616-1
DOI :
10.1109/NNSP.2002.1030021
