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- W1497291303 abstract "Semi-supervised learning is a machine learning framework where learning from data is conducted by utilizing a small amount of labeled data as well as a large amount of unlabeled data (Chapelle et al., 2006). It has been intensively studied in data mining and machine learning communities recently. One of the reasons is that, it can alleviate the time-consuming effort to collect “ground truth” labeled data while sustaining relatively high performance by exploiting a large amount of unlabeled data. (Blum & Mitchell, 1998) showed the PAC learnability of semi-supervised learning, especially in classification problem." @default.
- W1497291303 created "2016-06-24" @default.
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- W1497291303 date "2012-03-02" @default.
- W1497291303 modified "2023-09-27" @default.
- W1497291303 title "A Semi-Supervised Clustering Method Based on Graph Contraction and Spectral Graph Theory" @default.
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- W1497291303 doi "https://doi.org/10.5772/34371" @default.
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