Sequential Monte Carlo learning with hyperparameter adjustments

被引：0
|
作者：
Wada, K [1 ]
Yosui, K [1 ]
Nakada, Y [1 ]
Matsumoto, T [1 ]
机构：
[1] Waseda Univ, Dept Elect Elect & Comp Engn, Shinjuku Ku, Tokyo, Japan
来源：
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年
关键词：
D O I：
10.1109/IJCNN.2002.1005482
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Sequential Monte Carlo scheme is proposed for on-line 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.
引用
收藏
页码：274 / 279
页数：4
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