An online Bayesian filtering framework for Gaussian process regression: Application to global surface temperature analysis
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- 作者：Yali Wanga ; yl.wang@siat.ac.cn" class="auth_mail" title="E-mail the corresponding author ; Brahim Chaib-draa ; b ; chaib@ift.ulaval.ca" class="auth_mail" title="E-mail the corresponding author
- 关键词：Gaussian process regression ; Marginalized particle filter ; Online parameter learning ; Global surface temperature analysis
- 刊名：Expert Systems with Applications
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：67
- 期：Complete
- 页码：285-295
- 全文大小：1059 K
文摘
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A novel Bayesian filtering for GP regression, compared to other GP variants.
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It reduces computation while improving accuracy for large data sets.
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GP-based state space model processes data efficiently in a sequential manner.
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Our online learning mechanism is a novel venue for parameter optimization in GP.
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An efficient and accurate expert system for global surface temperature analysis.