Concepedia

Categorical Uncertainty Visualization

Categorical Uncertainty Visualization is a type of uncertainty visualization method that specifically represents uncertainty in categorical data by encoding the likelihood or confidence associated with discrete class memberships or category assignments.

2

Publications

63

Citations

11

Authors

6

Institutions

Publications per year

2017–2026

0

Parents

Publications

2

1–2 of 2

Authors

11

  1. JK
    Jorge Kazuo Yamamoto

    Universidade de São Paulo, Brazil

    1 publications · 26 citations

    1

    h-index

  2. HH
    Hexuan Hu

    Hohai University, China

    1 publications · 26 citations

    1

    h-index

  3. ÁP
    Álvaro Penteado Crósta

    Universidade Estadual de Campinas (UNICAMP), Brazil

    1 publications · 26 citations

    1

    h-index

  4. EA
    Erik Anderson

    University of Utah, United States

    1 publications · 37 citations

    1

    h-index

  5. SG
    Samuel Gerber

    University of Utah, United States

    1 publications · 37 citations

    1

    h-index

1–5 of 11

Institutions

6

  1. Universidade Estadual de Campinas (UNICAMP)

    Campinas, Brazil

    1 h-index

    1 publications

    26 citations

  2. Kyoto University

    Kyoto, Japan

    1 h-index

    1 publications

    26 citations

  3. Duke University

    Durham, United States

    1 h-index

    1 publications

    37 citations

  4. China Agricultural University

    Beijing, China

    1 h-index

    4 publications

    104 citations

  5. University of Utah

    Salt Lake City, United States

    1 h-index

    2 publications

    74 citations

  6. Universidade de São Paulo

    São Paulo, Brazil

    1 h-index

    2 publications

    52 citations

1–6 of 6

Venues

  1. Computers & Geosciences

    CAGEO · 1 publications · 26 citations

    1

    h-index

  2. IEEE Computer Graphics and Applications

    1 publications · 37 citations

    1

    h-index

1–2