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doi:10.22028/D291-37542
Title: | GUM2ALA – Uncertainty Propagation Algorithm for the Adaptive Linear Approximation According to the GUM |
Author(s): | Dorst, Tanja Schneider, Tizian Schütze, Andreas Eichstädt, Sascha |
Language: | English |
Title: | SMSI 2021 : Sensor and Measurement Science International : proceedings |
Pages: | 314-315 |
Publisher/Platform: | AMA Service GmbH |
Year of Publication: | 2021 |
Place of publication: | Wunstorf |
Place of the conference: | Online |
Free key words: | measurement uncertainty uncertainty propagation feature extraction adaptive linear approximation, machine learning |
DDC notations: | 600 Technology |
Publikation type: | Conference Paper |
Abstract: | In machine learning, many feature extraction algorithms are available. To obtain reliable features from measured data, a propagation of measurement uncertainty is necessary for these algorithms. In this contribution, the Adaptive Linear Approximation (ALA) as one feature extraction algorithm is considered, and analytical formulas are developed for an uncertainty propagation in line with the Guide to the Expression of Uncertainty in Measurement (GUM). This extends the set of uncertainty-aware feature extraction methods, which already contains the discrete Fourier and Wavelet transform. |
DOI of the first publication: | 10.5162/SMSI2021/D1.1 |
URL of the first publication: | https://www.ama-science.org/proceedings/details/4071 |
Link to this record: | urn:nbn:de:bsz:291--ds-375421 hdl:20.500.11880/33964 http://dx.doi.org/10.22028/D291-37542 |
ISBN: | 978-3-9819376-4-0 |
Date of registration: | 11-Oct-2022 |
Faculty: | NT - Naturwissenschaftlich- Technische Fakultät |
Department: | NT - Systems Engineering |
Professorship: | NT - Prof. Dr. Andreas Schütze |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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