Machine Learning

473.8k citations
2.9k papers · · active since 1950

Impact in

    • Machine Learning and Data Classification
    • Machine Learning and Algorithms
    • Imbalanced Data Classification Techniques
    • Neural Networks and Applications
    • Anomaly Detection Techniques and Applications
    • Text and Document Classification Technologies
    • Face and Expression Recognition

Papers in

    • Machine Learning and Algorithms 609
    • Machine Learning and Data Classification 426
    • Neural Networks and Applications 234
    • Bayesian Modeling and Causal Inference 216
    • Imbalanced Data Classification Techniques 214
    • Algorithms and Data Compression 196

Machine Learning

2.6k papers receiving 423.1k citations

Peers

Machine Learning
Comparison fields: 5 of 245
  • Artificial Intelligence 234.0k
  • Computer Vision and Pattern Recognition 71.2k
  • Signal Processing 34.6k
  • Information Systems 55.3k
  • Computational Theory and Mathematics 39.1k
Replace Journal of Machine Learning Research with:
Journal of Machine Learning Research United States
Neural Computation United States
ACM Computing Surveys United States
Artificial Intelligence United States
IEEE Transactions on Fuzzy Systems China
Journal of the Royal Statistical Society Series B (Statistical Methodology) United States
Information Fusion China
IEEE Transactions on Cybernetics China
The Annals of Statistics United States
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) United States
Machine Learning relative to Journal of Machine Learning Research United States Journal of Machine Learning Research's profile →
Citations per field
00.5×10×20×30×41.1×
Journal of Machine Learning Research · 1×
Citations per year

Countries where authors publish in Machine Learning

Since Specialization
Citations

This map shows the geographic impact of research published in Machine Learning. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Machine Learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Machine Learning more than expected).

Fields of papers published in Machine Learning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Machine Learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Machine Learning.

About Machine Learning

The 2.9k papers published in Machine Learning in the last decades have received a total of 473.8k indexed citations . Papers published in Machine Learning usually cover Artificial Intelligence (2.3k papers), Computational Mathematics (22 papers), Statistics and Probability (188 papers), Computational Theory and Mathematics (383 papers) and Computer Vision and Pattern Recognition (462 papers) specifically the topics of Machine Learning and Algorithms (609 papers), Machine Learning and Data Classification (426 papers), Neural Networks and Applications (234 papers), Data Mining Algorithms and Applications (229 papers), Bayesian Modeling and Causal Inference (216 papers), Imbalanced Data Classification Techniques (214 papers), Algorithms and Data Compression (196 papers) and Face and Expression Recognition (179 papers). The most active scholars publishing in Machine Learning are Leo Breiman, Vladimir Vapnik, Corinna Cortes, J. R. Quinlan, Robert E. Schapire, Peter Dayan, Richard S. Sutton, Christopher J. Watkins, Ronald J. Williams and Rich Caruana.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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