Instance-based Learning

Instance-Based Learning is a supervised learning paradigm in which the model classifies new instances by comparing them to stored training instances, relying on similarity measures rather than explicitly generalizing a function.

842

Publications

53.8K

Citations

2.7K

Authors

966

Institutions

Publications per year

2017–2026

370

Authors

2.7K

  1. CG
    Cleotilde González

    Carnegie Mellon University, United States

    22 publications · 1.3K citations

    17

    h-index

  2. VD
    Varun Dutt

    Indian Institute of Technology Mandi, India

    11 publications · 416 citations

    11

    h-index

  3. CL
    Christian Lebière

    Carnegie Mellon University, United States

    10 publications · 839 citations

    10

    h-index

  4. DW
    D.R. Wilson

    Brigham Young University, United States

    9 publications · 1.7K citations

    9

    h-index

  5. ZZ
    Zhi‐Hua Zhou

    Nanjing University, China

    8 publications · 566 citations

    8

    h-index

1–5 of 2.7K

Venues

  1. Pattern Recognition

    25 publications · 2.5K citations

    20

    h-index

  2. arXiv (Cornell University)

    Repository · 29 publications · 916 citations

    18

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 15 publications · 848 citations

    14

    h-index

  4. Machine Learning

    15 publications · 1.1K citations

    12

    h-index

  5. Information Sciences

    9 publications · 295 citations

    9

    h-index

1–5