Fast Binary Feature Selection with Conditional Mutual Information

585 indexed citations
published 2004

Countries where authors are citing Fast Binary Feature Selection with Conditional Mutual Information

Specialization
Citations

This map shows the geographic impact of Fast Binary Feature Selection with Conditional Mutual Information. 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 Fast Binary Feature Selection with Conditional Mutual Information with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fast Binary Feature Selection with Conditional Mutual Information more than expected).

Fields of papers citing Fast Binary Feature Selection with Conditional Mutual Information

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Fast Binary Feature Selection with Conditional Mutual Information. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Fast Binary Feature Selection with Conditional Mutual Information.

About Fast Binary Feature Selection with Conditional Mutual Information

This paper, published in 2004, received 585 indexed citations . Written by François Fleuret covering the research area of Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (328 citations), Computer Vision and Pattern Recognition (285 citations), Molecular Biology (126 citations), Signal Processing (67 citations) and Information Systems (42 citations). Published in Journal of Machine Learning Research.

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.

This paper is also available at doi.org/w3048823.

Explore hit-papers with similar magnitude of impact