Mixture Of Classifiers

Mixture of Classifiers is a classifier ensemble method that combines multiple base classifiers through weighted aggregation of their outputs, where the weights are typically learned from data to optimize classification performance.

517

Publications

54.4K

Citations

1.4K

Authors

600

Institutions

Publications per year

2017–2026

142

Authors

1.4K

  1. CY
    Ching Y. Suen

    Concordia University, Canada

    8 publications · 3.2K citations

    8

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  2. JG
    Joydeep Ghosh

    The University of Texas at Austin, United States

    5 publications · 724 citations

    5

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  3. RS
    Robert Sabourin

    École de Technologie Supérieure, Canada

    5 publications · 122 citations

    5

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  4. KW
    Kevin W. Bowyer

    University of Notre Dame, United States

    4 publications · 1.1K citations

    4

    h-index

  5. BK
    Bartosz Krawczyk

    Wrocław University of Science and Technology, Poland

    4 publications · 103 citations

    4

    h-index

1–5 of 1.4K

Venues

  1. Neurocomputing

    11 publications · 430 citations

    11

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  2. arXiv (Cornell University)

    Repository · 10 publications · 216 citations

    10

    h-index

  3. Pattern Recognition

    10 publications · 956 citations

    10

    h-index

  4. Expert Systems with Applications

    8 publications · 584 citations

    8

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  5. 8

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1–5