Positive-unlabeled Learning

Positive-Unlabeled Learning is a machine learning paradigm in which a classifier is trained using only positive and unlabeled data, differing from standard supervised learning by the absence of explicitly negative labels and relying on assumptions to infer the latent structure of the unlabeled data.

179

Publications

11.4K

Citations

625

Authors

282

Institutions

Publications per year

2017–2026

121

Authors

625

  1. MS
    Masashi Sugiyama

    Tokyo Institute of Technology, Japan

    9 publications · 410 citations

    9

    h-index

  2. XL
    Xiaoli Li

    Institute for Infocomm Research, Singapore

    7 publications · 1.2K citations

    7

    h-index

  3. PR
    Predrag Radivojac

    Indiana University Bloomington, United States

    6 publications · 198 citations

    6

    h-index

  4. SJ
    Shantanu Jain

    Northeastern University, United States

    6 publications · 198 citations

    6

    h-index

  5. SN
    See-Kiong Ng

    Institute for Infocomm Research, Singapore

    5 publications · 449 citations

    5

    h-index

1–5 of 625

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 328 citations

    9

    h-index

  2. BMC Bioinformatics

    8 publications · 303 citations

    8

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 241 citations

    6

    h-index

  4. Information Sciences

    5 publications · 129 citations

    5

    h-index

  5. Pattern Recognition Letters

    4 publications · 126 citations

    4

    h-index

1–5