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- W4247647416 abstract "Developing machine learning algorithms for applications of Internet-of-Things requires collecting a large amount of labeled training data, which is an expensive and labor-intensive process. Upon a minor change in the context, for example utilization by a new user, the model will need re-training to maintain the initial performance. To address this problem, we propose a graph model and an unsupervised label transfer algorithm (learn-on-the-go) which exploits the relations between source and target user data to develop a highly-accurate and scalable machine learning model. Our analysis on real-world data demonstrates 54% and 22% performance improvement against baseline and state-of-the-art solutions, respectively." @default.
- W4247647416 created "2022-05-12" @default.
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- W4247647416 date "2017-11-01" @default.
- W4247647416 modified "2023-09-24" @default.
- W4247647416 title "Learn-on-the-go: Autonomous cross-subject context learning for internet-of-things applications" @default.
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- W4247647416 doi "https://doi.org/10.1109/iccad.2017.8203800" @default.
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