Online sparse Gaussian process regression using FITC and PITC approximations**This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs. The work was also supported by the Swedish research Council (VR) via the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524).-Reference-Cited by-同舟云学术

Online sparse Gaussian process regression using FITC and PITC approximations**This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs. The work was also supported by the Swedish research Council (VR) via the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524).

Published:2015 Issue:28 Volume:48 Page:703-708
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Bijl Hildo,van Wingerden Jan-Willem,B. Schön Thomas,Verhaegen Michel

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference15 articles.

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2. Yanshuai Cao, Marcus A. Brubaker, David Fleet, and Aaron Hertzmann. Eficient optimization for sparse Gaussian process regression. In Advances in Neural Information Processing Systems 26, pages 1097-1105, 2013.

3. A framework for evaluating approximation methods for Gaussian process regression;Krzysztof;Journal of Machine Learning Research (JMLR),2002

4. Sparse online Gaussian processes;Legel;Neural Computation,2002

5. Updating the inverse of a matrix;William;SIAM Rev,1989

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