The Relevance Vector Machine
Impact in
Classified as
- Authors
- Michael E. Tipping
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w5583799 →Countries where authors are citing The Relevance Vector Machine
This map shows the geographic impact of The Relevance Vector Machine. 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 The Relevance Vector Machine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The Relevance Vector Machine more than expected).
Fields of papers citing The Relevance Vector Machine
This network shows the impact of The Relevance Vector Machine. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The Relevance Vector Machine.
About The Relevance Vector Machine
This paper, published in 1999, received 596 indexed citations . Written by Michael E. Tipping covering the research area of Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (206 citations), Computer Vision and Pattern Recognition (167 citations), Control and Systems Engineering (103 citations), Electrical and Electronic Engineering (59 citations) and Signal Processing (56 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/w5583799.