Active Learning For Regression

Active Learning for Regression is an active learning approach that specifically addresses regression tasks by iteratively selecting the most informative instances to label in order to improve the performance of a regression model with minimal labeled data.

108

Publications

6.2K

Citations

300

Authors

152

Institutions

Publications per year

2017–2026

66

Authors

300

  1. DW
    Dongrui Wu

    Huazhong University of Science and Technology, China

    5 publications · 429 citations

    5

    h-index

  2. XY
    Xufeng Yang

    Southwest Jiaotong University, China

    4 publications · 101 citations

    4

    h-index

  3. MS
    Masashi Sugiyama

    Tokyo Institute of Technology, Japan

    4 publications · 267 citations

    4

    h-index

  4. FM
    Farid Melgani

    University of Trento, Italy

    3 publications · 115 citations

    3

    h-index

  5. EP
    Edoardo Pasolli

    University of Trento, Italy

    3 publications · 115 citations

    3

    h-index

1–5 of 300

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 132 citations

    6

    h-index

  2. Reliability Engineering & System Safety

    5 publications · 214 citations

    5

    h-index

  3. Information Sciences

    5 publications · 310 citations

    5

    h-index

  4. Structural Safety

    3 publications · 152 citations

    3

    h-index

  5. Structural and Multidisciplinary Optimization

    3 publications · 58 citations

    3

    h-index

1–5