Less is More: Active Learning with Support Vector Machines
Impact in
Classified as
- Authors
- David Cohn
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w11685859 →Countries where authors are citing Less is More: Active Learning with Support Vector Machines
This map shows the geographic impact of Less is More: Active Learning with 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 Less is More: Active Learning with 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 Less is More: Active Learning with Support Vector Machines more than expected).
Fields of papers citing Less is More: Active Learning with Support Vector Machines
This network shows the impact of Less is More: Active Learning with 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 Less is More: Active Learning with Support Vector Machines.
About Less is More: Active Learning with Support Vector Machines
This paper, published in 2000, received 529 indexed citations . Written by David Cohn covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (433 citations), Computer Vision and Pattern Recognition (174 citations), Media Technology (52 citations), Control and Systems Engineering (37 citations) and Signal Processing (33 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/w11685859.