Support Vector Method for Novelty Detection
Impact in
Classified as
- Journal
- UCL Discovery (University College London)
In The Last Decade
doi.org/w2873294 →Countries where authors are citing Support Vector Method for Novelty Detection
This map shows the geographic impact of Support Vector Method for Novelty Detection. 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 Support Vector Method for Novelty Detection with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Support Vector Method for Novelty Detection more than expected).
Fields of papers citing Support Vector Method for Novelty Detection
This network shows the impact of Support Vector Method for Novelty Detection. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Support Vector Method for Novelty Detection.
About Support Vector Method for Novelty Detection
This paper, published in 1999, received 1.4k indexed citations . Written by Bernhard Schölkopf, Robert C. Williamson, Alex Smola, John Shawe‐Taylor and John Platt covering the research area of Control and Systems Engineering and Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (984 citations), Computer Networks and Communications (380 citations), Control and Systems Engineering (256 citations), Signal Processing (252 citations) and Computer Vision and Pattern Recognition (225 citations). Published in UCL Discovery (University College London).
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/w2873294.