Co-training

Co-Training is a semi-supervised learning method that leverages two conditionally independent views of the data to iteratively train two classifiers, each using the high-confidence predictions of the other to expand the labeled training set.

360

Publications

31.8K

Citations

1.2K

Authors

395

Institutions

Publications per year

2017–2026

181

Authors

1.2K

  1. ZZ
    Zhi‐Hua Zhou

    Nanjing University, China

    6 publications · 952 citations

    6

    h-index

  2. IK
    Irena Koprinska

    The University of Sydney, Australia

    5 publications · 115 citations

    5

    h-index

  3. AY
    Alan Yuille

    Johns Hopkins University, United States

    5 publications · 650 citations

    5

    h-index

  4. IW
    Ivor W. Tsang

    University of Technology Sydney, Australia

    5 publications · 1.9K citations

    5

    h-index

  5. CC
    Cornelia Caragea

    Pennsylvania State University, United States

    4 publications · 83 citations

    4

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 29 publications · 3K citations

    17

    h-index

  2. IEEE Access

    6 publications · 221 citations

    7

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 343 citations

    7

    h-index

  4. IEEE Transactions on Image Processing

    6 publications · 310 citations

    6

    h-index

  5. Information Sciences

    4 publications · 513 citations

    5

    h-index

1–5