Probabilistic Classification

Probabilistic Classification is a supervised learning method that assigns class labels by modeling the probability distribution over possible output classes given an input, distinguishing itself from deterministic classifiers through its explicit representation of uncertainty in predictions.

906

Publications

76.6K

Citations

2.4K

Authors

935

Institutions

Publications per year

2017–2026

195

Authors

2.4K

  1. BJ
    Barbara J. Knowlton

    University of California, Los Angeles, United States

    9 publications · 3K citations

    9

    h-index

  2. MS
    Masashi Sugiyama

    Tokyo Institute of Technology, Japan

    8 publications · 431 citations

    8

    h-index

  3. TL
    Tongliang Liu

    The University of Sydney, Australia

    7 publications · 1.1K citations

    7

    h-index

  4. PM
    Paul M. Baggenstoss

    Naval Undersea Warfare Center, United States

    6 publications · 365 citations

    6

    h-index

  5. ER
    Edward R. Dougherty

    Texas A&M University, United States

    5 publications · 295 citations

    5

    h-index

1–5 of 2.4K

Venues

  1. arXiv (Cornell University)

    Repository · 40 publications · 2K citations

    22

    h-index

  2. Pattern Recognition

    18 publications · 885 citations

    15

    h-index

  3. IEEE Transactions on Neural Networks

    15 publications · 1.8K citations

    15

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 16 publications · 1.8K citations

    14

    h-index

  5. IEEE Transactions on Information Theory

    15 publications · 3.6K citations

    14

    h-index

1–5