Expected Error Reduction

Expected Error Reduction is an active learning criterion that belongs to the class of query strategies and is distinguished by its objective of selecting unlabeled instances that minimize the expected generalization error of the model after labeling.

6

Publications

416

Citations

21

Authors

7

Institutions

Publications per year

2017–2026

3

Authors

21

  1. ND
    Neill D. F. Campbell

    University of Bath, United Kingdom

    1 publications · 98 citations

    1

    h-index

  2. XW
    Xindong Wu

    Hefei University of Technology, China

    1 publications · 42 citations

    1

    h-index

  3. VG
    Vaibhava Goel

    IBM (United States), United States

    1 publications · 18 citations

    1

    h-index

  4. WG
    Weikai Gao

    Tsinghua University, China

    1 publications · 78 citations

    1

    h-index

  5. HJ
    Hong-Kwang Jeff Kuo

    IBM Research - Thomas J. Watson Research Center, United States

    1 publications · 18 citations

    1

    h-index

1–5 of 21

Venues

  1. Bern Open Repository and Information System (University of Bern)

    Repository · 1 publications · 139 citations

    1

    h-index

  2. Reliability Engineering & System Safety

    1 publications · 78 citations

    1

    h-index

1–2