DocumentCode
1645483
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
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
274
Lastpage
279
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1005482
Filename
1005482
Link To Document