Soft Boundary Estimation

Soft Boundary Estimation is a methodological framework in estimation theory that characterizes uncertainty regions with probabilistic or graded membership functions, distinguishing it from crisp or binary boundary models by allowing gradual transitions between included and excluded domains.

9

Publications

531

Citations

19

Authors

12

Institutions

Publications per year

2017–2026

2

Authors

19

  1. JS
    Jizhou Sun

    Tianjin University, China

    1 publications · 21 citations

    1

    h-index

  2. WY
    Weishi Yin

    Changchun University of Science and Technology, China

    1 publications · 22 citations

    1

    h-index

  3. NA
    Nisar Ahmed

    University of Colorado Boulder, United States

    1 publications · 20 citations

    1

    h-index

  4. SM
    Su Mei Lin

    China University of Technology, Taiwan

    1 publications · 22 citations

    1

    h-index

  5. EE
    Etienne E. Kerre

    Ghent University, Belgium

    1 publications · 12 citations

    1

    h-index

1–5 of 19

Venues

  1. Pattern Analysis and Applications

    1 publications · 19 citations

    1

    h-index

  2. IEEE Signal Processing Letters

    1 publications · 20 citations

    1

    h-index

  3. Journal of Mathematical Physics

    1 publications · 151 citations

    1

    h-index

  4. IEEE Robotics and Automation Letters

    1 publications · 54 citations

    1

    h-index

  5. Alexandria Engineering Journal

    AEJ · 1 publications · 22 citations

    1

    h-index

1–5