Title :
Sequential Monte Carlo learning with hyperparameter adjustments
Author :
Wada, K. ; Yosui, K. ; Nakada, Y. ; Matsumoto, T.
Author_Institution :
Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan
fDate :
6/24/1905 12:00:00 AM
Abstract :
Sequential Monte Carlo scheme is proposed for online Bayesian learning. The proposed scheme adjusts not only parameters for data fitting but adjust hyperparameters online so that the scheme attempts to avoid over fitting in an adaptive manner. The scheme is tested against simple examples and is shown to be functional
Keywords :
Bayes methods; Monte Carlo methods; learning (artificial intelligence); neural nets; data fitting; hyperparameter adjustments; online Bayesian learning; parameter adjustments; sequential Monte Carlo learning; Bayesian methods; Distributed computing; Monte Carlo methods; Nonlinear equations; Sequential analysis; State estimation; Testing; Training data; Uncertainty; Yttrium;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005482
