Vector Scaling

Vector Scaling is a post-hoc calibration method that belongs to the class of parametric scaling techniques, distinguished by its application of a learned weight vector and bias term to the logit outputs of a model in order to optimize the predicted probability distribution for improved alignment with true class likelihoods.

13

Publications

653

Citations

35

Authors

21

Institutions

Publications per year

2017–2026

1

Authors

35

  1. WS
    Wayne S. DeSarbo

    Pennsylvania State University, United States

    3 publications · 85 citations

    3

    h-index

  2. TP
    Thomas P. Urbach

    University of California San Diego, United States

    2 publications · 311 citations

    2

    h-index

  3. MK
    Marta Kutas

    University of California San Diego, United States

    2 publications · 311 citations

    2

    h-index

  4. HL
    Houqiang Li

    University of Science and Technology of China, China

    1 publications · 30 citations

    1

    h-index

  5. QW
    Qiong Wang

    University of Oklahoma, United States

    1 publications · 14 citations

    1

    h-index

1–5 of 35

Venues

  1. 1

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 12 citations

    1

    h-index

  3. 1

    h-index

  4. Theoretical and Mathematical Physics

    1 publications · 24 citations

    1

    h-index

  5. Pattern Recognition Letters

    1 publications · 14 citations

    1

    h-index

1–5