Structured Support Vector Machines

Structured Support Vector Machines is a discriminative machine learning model that extends the standard support vector machine framework to structured output spaces by jointly learning a scoring function over input-output pairs and optimizing prediction under a structured loss function.

103

Publications

5.1K

Citations

276

Authors

131

Institutions

Publications per year

2017–2026

22

Authors

276

  1. MG
    Mark Gales

    University of Cambridge, United Kingdom

    4 publications · 110 citations

    4

    h-index

  2. SZ
    Shi-Xiong Zhang

    Bellevue Hospital Center, United States

    4 publications · 140 citations

    4

    h-index

  3. CH
    Christoph H. Lampert

    Institute of Science and Technology Austria, Austria

    3 publications · 80 citations

    3

    h-index

  4. AB
    Antoni B. Chan

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 264 citations

    3

    h-index

  5. SN
    Sebastian Nowozin

    Microsoft Research (United Kingdom), United Kingdom

    3 publications · 282 citations

    3

    h-index

1–5 of 276

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 207 citations

    8

    h-index

  2. 4

    h-index

  3. 4

    h-index

  4. Neural Networks

    3 publications · 51 citations

    3

    h-index

  5. IEEE Transactions on Image Processing

    2 publications · 47 citations

    2

    h-index

1–5