Statistical Learning

Statistical learning is a machine learning framework that uses formal statistical methods and assumptions to model the functional relationship between input variables and output responses, enabling both prediction and statistical inference.

29.7K

Publications

3M

Citations

66.8K

Authors

8.7K

Institutions

Publications per year

2017–2026

13.5K

Authors

66.8K

  1. YB
    Yoshua Bengio

    Université de Montréal, Canada

    60 publications · 10.3K citations

    40

    h-index

  2. TH
    Trevor Hastie

    Stanford University, United States

    47 publications · 58.7K citations

    40

    h-index

  3. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    57 publications · 36.1K citations

    39

    h-index

  4. MS
    Masashi Sugiyama

    Tokyo Institute of Technology, Japan

    94 publications · 6.2K citations

    39

    h-index

  5. JA
    Johan A. K. Suykens

    KU Leuven, Belgium

    64 publications · 5.1K citations

    38

    h-index

1–5 of 66.8K

Venues

  1. arXiv (Cornell University)

    Repository · 2.1K publications · 134.2K citations

    155

    h-index

  2. Journal of the American Statistical Association

    275 publications · 65.3K citations

    104

    h-index

  3. The Annals of Statistics

    200 publications · 91.4K citations

    91

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 284 publications · 47.5K citations

    91

    h-index

  5. IEEE Transactions on Neural Networks

    227 publications · 40.3K citations

    86

    h-index

1–5

See also

Statistical Learning

Core Methods

Theoretical Foundations

Evaluation

Applications