Semi-supervised Learning

Semi-Supervised Learning is a machine learning paradigm in which a predictor is trained on a combination of a relatively small labeled dataset and a large unlabeled dataset, exploiting the structure of the unlabeled data to improve learning performance relative to purely supervised or unsupervised approaches.

7.8K

Publications

573K

Citations

19.4K

Authors

3.1K

Institutions

Publications per year

2017–2026

5.1K

Authors

19.4K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    77 publications · 6.8K citations

    41

    h-index

  2. ZZ
    Zhi‐Hua Zhou

    Nanjing University, China

    41 publications · 4.3K citations

    32

    h-index

  3. DT
    Dacheng Tao

    The University of Sydney, Australia

    34 publications · 3K citations

    28

    h-index

  4. LJ
    Licheng Jiao

    Xidian University, China

    56 publications · 1.9K citations

    27

    h-index

  5. XZ
    Xiaojin Zhu

    University of Wisconsin–Madison, United States

    28 publications · 11.2K citations

    23

    h-index

1–5 of 19.4K

Venues

  1. arXiv (Cornell University)

    Repository · 617 publications · 77K citations

    100

    h-index

  2. IEEE Transactions on Geoscience and Remote Sensing

    143 publications · 14.8K citations

    62

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 96 publications · 13.9K citations

    57

    h-index

  4. 51

    h-index

  5. Pattern Recognition

    150 publications · 9.2K citations

    50

    h-index

1–5