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

页码：292 / 299

页数：8

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