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This research aims to improve the performance of semi-supervised learning by automatically select unlabeled data. The proposed method uses two regression models to estimate values for unlabeled data, then cluster the data into groups. Therefore, similar data are assigned in the same group and the different data are assigned into the different groups. After that, the method selects each group representative that have least error and append into training data. Then, we repeat until we have enough training data. From experimental results with three datasets, we found that the proposed method can improve performance and reduce computation time by 84%, comparing to previous work.
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