Gaussian Process Classification

Gaussian Process Classification is a probabilistic classification framework that employs Gaussian processes as the prior distribution over latent functions, distinguishing itself by modeling class probabilities through a likelihood function suitable for discrete outcomes.

174

Publications

13.1K

Citations

518

Authors

266

Institutions

Publications per year

2017–2026

62

Authors

518

  1. MS
    Matthias Seeger

    Max Planck Society, Germany

    7 publications · 646 citations

    7

    h-index

  2. ZG
    Zoubin Ghahramani

    University of Cambridge, United Kingdom

    6 publications · 1K citations

    6

    h-index

  3. AF
    André F. Marquand

    Radboud University Nijmegen, Netherlands

    5 publications · 411 citations

    5

    h-index

  4. MO
    Manfred Opper

    Technische Universität Berlin, Germany

    5 publications · 315 citations

    5

    h-index

  5. OW
    Ole Winther

    Technical University of Denmark, Denmark

    4 publications · 298 citations

    4

    h-index

1–5 of 518

Venues

  1. arXiv (Cornell University)

    Repository · 21 publications · 923 citations

    14

    h-index

  2. IEEE Access

    5 publications · 171 citations

    5

    h-index

  3. Infoscience (Ecole Polytechnique Fédérale de Lausanne)

    Repository · 5 publications · 249 citations

    5

    h-index

  4. Journal of Machine Learning Research

    JMLR · 4 publications · 621 citations

    4

    h-index

  5. PLoS ONE

    3 publications · 238 citations

    3

    h-index

1–5