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• W3089791131 abstract "In the context of self-adaptive and self-organising systems, the adaptation of spontaneously available knowledge is a key to improve a system's learning quality in detecting novelties or improving the accuracy in general. We introduce an opportunistic meta-learning architecture capable of evaluating and ranking knowledge sources at runtime and to adapt to the most promising one. Furthermore, we present experimental simulations in the domain of quality assurance and show that adaptive knowledge can stabilise the learning procedure." @default.
• W3089791131 created "2020-10-08" @default.
• W3089791131 creator A5043556194 @default.
• W3089791131 creator A5062428988 @default.
• W3089791131 date "2020-08-01" @default.
• W3089791131 modified "2023-10-18" @default.
• W3089791131 title "Opportunistic Meta-Learning: A Case Study for Quality Assurance in Industry 4.0 Environments" @default.
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• W3089791131 doi "https://doi.org/10.1109/acsos-c51401.2020.00034" @default.
• W3089791131 hasPublicationYear "2020" @default.
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