Redundancy Detection

Redundancy Detection is a constraint modeling method that identifies structurally redundant elements within a system by distinguishing between essential and non-essential components based on their contribution to the overall constraint satisfaction.

99

Publications

5.6K

Citations

280

Authors

143

Institutions

Publications per year

2017–2026

14

Authors

280

  1. HJ
    Hong Jiang

    University of Nebraska–Lincoln, United States

    2 publications · 129 citations

    2

    h-index

  2. DF
    Dan Feng

    Huazhong University of Science and Technology, China

    2 publications · 129 citations

    2

    h-index

  3. TH
    Thomas H. Kerr

    MIT Lincoln Laboratory, United States

    2 publications · 179 citations

    2

    h-index

  4. TP
    Thomas P. Minka

    Carnegie Mellon University, United States

    2 publications · 475 citations

    2

    h-index

  5. DA
    David A. Harrison

    The University of Texas at Arlington, United States

    2 publications · 66 citations

    2

    h-index

1–5 of 280

Venues

  1. 2

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  2. Psychological Bulletin

    2 publications · 94 citations

    2

    h-index

  3. IEEE Transactions on Computers

    2 publications · 126 citations

    2

    h-index

  4. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 2 publications · 22 citations

    2

    h-index

  5. Perception & Psychophysics

    2 publications · 162 citations

    2

    h-index

1–5