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1
Quantitative Decomposition of Prediction Errors Revealing M..:
, In:
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
,
Sakuma, Keita
;
Matsuno, Ryuta
;
Kameda, Yoshio
- p. 4259-4263 , 2023
Link:
https://dl.acm.org/doi/10.1145/3583780.3615238
RT T1 <cTitle>Proceedings of the 32nd ACM International Conference on Information and Knowledge Management</cTitle>: T1 <Title>Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3615238&Exemplar=1&LAN=DE A1 Sakuma, Keita A1 Matsuno, Ryuta A1 Kameda, Yoshio PB ACM YR 2023 K1 error analysis K1 interpretability K1 machine learning K1 mlops K1 Computing methodologies K1 Machine learning K1 Software and its engineering K1 Software creation and management K1 Software post-development issues K1 Maintaining software K1 Software verification and validation K1 Operational analysis SP 4259 OP 4263 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3583780.3615238 DO https://dl.acm.org/doi/10.1145/3583780.3615238 SF ELIB - SuUB Bremen
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