文章基本信息

标题：Sparse Bayesian Learning for Nonstationary Data Sources
本地全文：下载
作者：Ryohei Fujimaki ; Takehisa Yairi ; Kazuo Machida 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2008
卷号：23
期号：1
页码：50-57
DOI：10.1527/tjsai.23.50
出版社：The Japanese Society for Artificial Intelligence
摘要：This paper proposes an online Sparse Bayesian Learning (SBL) algorithm for modeling nonstationary data sources. Although most learning algorithms implicitly assume that a data source does not change over time (stationary), one in the real world usually does due to such various factors as dynamically changing environments, device degradation, sudden failures, etc (nonstationary). The proposed algorithm can be made useable for stationary online SBL by setting time decay parameters to zero, and as such it can be interpreted as a single unified framework for online SBL for use with stationary and nonstationary data sources. Tests both on four types of benchmark problems and on actual stock price data have shown it to perform well.
关键词：Sparse Bayesian Learning ; Nonstationary Data