Tackling the poor assumptions of naive bayes text classifiers
Impact in
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- International Conference on Machine Learning
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doi.org/w5367741 →Countries where authors are citing Tackling the poor assumptions of naive bayes text classifiers
This map shows the geographic impact of Tackling the poor assumptions of naive bayes text classifiers. 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 Tackling the poor assumptions of naive bayes text classifiers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tackling the poor assumptions of naive bayes text classifiers more than expected).
Fields of papers citing Tackling the poor assumptions of naive bayes text classifiers
This network shows the impact of Tackling the poor assumptions of naive bayes text classifiers. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Tackling the poor assumptions of naive bayes text classifiers.
About Tackling the poor assumptions of naive bayes text classifiers
This paper, published in 2003, received 630 indexed citations . Written by Jason D. M. Rennie, Lawrence Shih, Jaime Teevan and David R. Karger covering the research area of Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (430 citations), Information Systems (214 citations), Molecular Biology (65 citations), Computer Vision and Pattern Recognition (59 citations) and Signal Processing (49 citations). Published in International Conference on Machine Learning.
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/w5367741.