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- W1555816091 abstract "Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates." @default.
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- W1555816091 date "2015-06-01" @default.
- W1555816091 modified "2023-09-25" @default.
- W1555816091 title "Semi-supervised Learning for the Positioning of a Smartphone-based Robot" @default.
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- W1555816091 doi "https://doi.org/10.5302/j.icros.2015.14.0121" @default.
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