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Paper K. Recursive Gaussian Process: On-line Regression and Learning. Edited version of the paper: M. F. Huber. Recursive Gaussian Process: On-line Regression and Learning. Pattern Recognition Letters, vol. 45, pages 85-91, August 2014

Huber, Marco F.

Two approaches for on-line Gaussian process regression with low computational and memory demands are proposed. The first approach assumes known hyperparameters and performs regression on a set of basis vectors that stores mean and covariance estimates of the latent function. The second approach additionally learns the hyperparameters on-line. For this purpose, techniques fromnonlinear Gaussian state estimation are exploited. The proposed approaches are compared to state-of-the-art sparse Gaussian process algorithms.

Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Buchaufsatz
Jahr 2015
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-460725
KITopen-ID: 1000046072
Erschienen in Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications. Ed.: M. Huber
Verlag KIT, Karlsruhe
Seiten 446-466
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