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- W2258852083 abstract "Sensor-based human activity recognition aims to automatically identify human activities from a series of sensor observations, which is a crucial task for supporting wide range applications. Typically, given sufficient training examples for all activities (or activity classes), supervised learning techniques have been applied to build a classification model using sufficient training samples for differentiating various activities. However, it is often impractical to manually label large amounts of training data for each individual activities. As such, semi-supervised learning techniques sound promising alternatives as they have been designed to utilize a small training set L, enhanced by a large unlabeled set U. However, we observe that directly applying semi-supervised learning techniques may not produce accurate classification. In this paper, we have designed a novel dynamic temporal extension technique to extend L into a bigger training set, and then build a final semi-supervised learning model for more accurate classification. Extensive experiments demonstrate that our proposed technique outperforms existing 7 state-of-the-art supervised learning and semi-supervised learning techniques." @default.
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- W2258852083 date "2014-01-01" @default.
- W2258852083 modified "2023-10-16" @default.
- W2258852083 title "Activity Recognition Using a Few Label Samples" @default.
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- W2258852083 doi "https://doi.org/10.1007/978-3-319-06608-0_43" @default.
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