Relevance Vector Machines

Relevance Vector Machines is a sparse Bayesian learning model that, as a probabilistic counterpart to Support Vector Machines, identifies a minimal set of relevance vectors to achieve parsimonious regression and classification by leveraging Bayesian inference for automatic relevance determination.

685

Publications

41.8K

Citations

2K

Authors

732

Institutions

Publications per year

2017–2026

275

Authors

2K

  1. PS
    Pijush Samui

    National Institute of Technology Patna, India

    20 publications · 896 citations

    15

    h-index

  2. MM
    Mac McKee

    Utah State University, United States

    12 publications · 693 citations

    12

    h-index

  3. DL
    Datong Liu

    Harbin Institute of Technology, China

    9 publications · 587 citations

    9

    h-index

  4. KG
    Kai Goebel

    Ames Research Center, United States

    7 publications · 1.8K citations

    7

    h-index

  5. RN
    Robert N. Weinreb

    University of California San Diego, United States

    6 publications · 350 citations

    6

    h-index

1–5 of 2K

Venues

  1. Sensors

    13 publications · 506 citations

    13

    h-index

  2. IEEE Access

    13 publications · 549 citations

    12

    h-index

  3. Energies

    9 publications · 241 citations

    9

    h-index

  4. Expert Systems with Applications

    8 publications · 690 citations

    8

    h-index

  5. IEEE Sensors Journal

    7 publications · 217 citations

    7

    h-index

1–5