Support Vector Regression

Support Vector Regression is a regression method that extends the principles of Support Vector Machines by employing structural risk minimization and kernel functions to predict continuous outcomes through the identification of a function that deviates from the observed data by no more than a specified margin, while maximizing the generalizability of the model.

8.7K

Publications

479.9K

Citations

29.5K

Authors

5.6K

Institutions

Publications per year

2017–2026

5.2K

Authors

29.5K

  1. SS
    Shahaboddin Shamshirband

    University of Malaya, Malaysia

    35 publications · 3.1K citations

    27

    h-index

  2. ÖK
    Özgür Kişi

    Ilia State University, Georgia

    26 publications · 1.8K citations

    22

    h-index

  3. WH
    Wei‐Chiang Hong

    Asia Eastern University of Science and Technology, Taiwan

    24 publications · 2.6K citations

    21

    h-index

  4. AM
    Amir Mosavi

    Obuda University, Hungary

    17 publications · 1.3K citations

    17

    h-index

  5. TO
    Taoreed O. Owolabi

    Adekunle Ajasin University, Nigeria

    25 publications · 740 citations

    17

    h-index

1–5 of 29.5K

Venues

  1. Expert Systems with Applications

    107 publications · 10.8K citations

    50

    h-index

  2. IEEE Access

    151 publications · 7.5K citations

    48

    h-index

  3. Remote Sensing

    133 publications · 7.1K citations

    48

    h-index

  4. Scientific Reports

    122 publications · 6.2K citations

    46

    h-index

  5. Neurocomputing

    76 publications · 7K citations

    43

    h-index

1–5