Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid
Impact in
Classified as
- Authors
- Ron Kohavi
- Journal
- Knowledge Discovery and Data Mining
In The Last Decade
doi.org/w9559592 →Countries where authors are citing Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid
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Fields of papers citing Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid
This network shows the impact of Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid.
About Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid
This paper, published in 1996, received 834 indexed citations . Written by Ron Kohavi covering the research area of Computational Theory and Mathematics, Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (593 citations), Information Systems (233 citations), Computer Vision and Pattern Recognition (94 citations), Computer Networks and Communications (92 citations) and Computational Theory and Mathematics (84 citations). Published in Knowledge Discovery and Data Mining.
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/w9559592.