Exploiting the Availability-Continuity Trade-off in Imperfect Retraining of Machine Learning Systems

Machine Learning Systems (MLSs) often combine diverse models to achieve complex objectives but face performance degradation due to dataset shifts. Regular performance monitoring and model retraining are essential to mitigate this risk. However, model retraining may not always fully restore the syste...

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Vydané v:	Proceedings - International Symposium on Software Reliability Engineering s. 406 - 417
Hlavní autori:	Wang, Zhengji, Machida, Fumio
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 21.10.2025
Predmet:	Computational modeling dataset shift Degradation Faces Machine learning machine learning system Maintenance Numerical models retraining semi-Markov process service availability Software reliability Stability analysis Switched mode power supplies Three-dimensional displays
ISSN:	2332-6549
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