On-line learning methods for Gaussian processes

被引：0
|
作者：
Oba, S [1 ]
Sato, M
Ishii, S
机构：
[1] Nara Inst Sci & Technol, Takayama, Ikoma 89165, Japan
[2] ATR Int, Kyoto, Japan
来源：
ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS | 2001年 / 2130卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This article proposes two modifications of Gaussian processes, which aim to deal with dynamic environments. One is a weight decay method that gradually forgets the old data, and the other is a time stamp method that regards the time course of data as a Gaussian process. We show experimental results when these modifications are applied to regression problems in dynamic environments.
引用
收藏
页码：292 / 299
页数：8
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