Classical Machine Learning

Classical Machine Learning is a machine learning paradigm that relies on handcrafted features and traditional statistical models—such as logistic regression, support vector machines, and decision trees—distinguished from deep learning by its independence from deep neural networks and large-scale automatic feature extraction.

728

Publications

41.5K

Citations

2.6K

Authors

1.1K

Institutions

Publications per year

2017–2026

598

Variants

Traditional Machine Learning

Authors

2.6K

  1. MS
    Maria Schuld

    University of KwaZulu-Natal, South Africa

    6 publications · 982 citations

    6

    h-index

  2. RV
    R. Vinayakumar

    Amrita Vishwa Vidyapeetham, India

    5 publications · 140 citations

    5

    h-index

  3. EN
    Emile Ndikumana

    Université de Montpellier, France

    4 publications · 477 citations

    4

    h-index

  4. DH
    Dinh Ho Tong Minh

    Territoires, Environnement, Télédétection et Information Spatiale

    4 publications · 477 citations

    4

    h-index

  5. RU
    Rehan Ullah Khan

    Qassim University, Saudi Arabia

    4 publications · 173 citations

    4

    h-index

1–5 of 2.6K

Venues

  1. IEEE Access

    28 publications · 2.4K citations

    23

    h-index

  2. arXiv (Cornell University)

    Repository · 56 publications · 1.6K citations

    22

    h-index

  3. Scientific Reports

    15 publications · 616 citations

    15

    h-index

  4. Sensors

    14 publications · 643 citations

    14

    h-index

  5. Applied Sciences

    14 publications · 574 citations

    12

    h-index

1–5