Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Impact in
Classified as
- Authors
- Thorsten Joachims
- Journal
- Kluwer Academic Publishers eBooks
In The Last Decade
doi.org/w7765617 →Countries where authors are citing Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
This map shows the geographic impact of Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. 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 Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms more than expected).
Fields of papers citing Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
This network shows the impact of Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms.
About Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
This paper, published in 2002, received 642 indexed citations . Written by Thorsten Joachims covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (416 citations), Computer Vision and Pattern Recognition (142 citations), Information Systems (139 citations), Molecular Biology (87 citations) and Signal Processing (62 citations). Published in Kluwer Academic Publishers eBooks.
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/w7765617.