Semi-Supervised Support Vector Machines
- Authors
- Kristin P. BennettAyhan Demiriz
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w6103911 →Countries where authors are citing Semi-Supervised Support Vector Machines
This map shows the geographic impact of Semi-Supervised Support Vector Machines. 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 Semi-Supervised Support Vector Machines with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Semi-Supervised Support Vector Machines more than expected).
Fields of papers citing Semi-Supervised Support Vector Machines
This network shows the impact of Semi-Supervised Support Vector Machines. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Semi-Supervised Support Vector Machines.
About Semi-Supervised Support Vector Machines
This paper, published in 1998, received 557 indexed citations . Written by Kristin P. Bennett and Ayhan Demiriz covering the research area of Artificial Intelligence, Analytical Chemistry and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (393 citations), Computer Vision and Pattern Recognition (247 citations) and Media Technology (45 citations). Published in Neural Information Processing Systems.
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/w6103911.