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Titel:

Gaussian Process-Based Real-Time Learning for Safety Critical Applications

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
A. Lederer; A. J. Ordóñez Conejo; K. Maier; W. Xiao; J. Umlauft; S. Hirche
Seitenangaben Beitrag:
6055-6064
Abstract:
The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its high computational complexity, Gaussian process regression must be used offline on batches of data, which prevents applications, where a fast adaptation through online learning is necessary to ensure saf...     »
Stichworte:
data_driven_control; coman
Kongress- / Buchtitel:
Proceedings of the 38th International Conference on Machine Learning
Jahr:
2021
Jahr / Monat:
2021-07
Monat:
Jul
Serientitel:
Proceedings of Machine Learning Research
Serienbandnummer:
139
WWW:
http://proceedings.mlr.press/v139/lederer21a.html
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