Large Margin Methods for Structured and Interdependent Output Variables
In The Last Decade
doi.org/w5479647 →Countries where authors are citing Large Margin Methods for Structured and Interdependent Output Variables
This map shows the geographic impact of Large Margin Methods for Structured and Interdependent Output Variables. 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 Large Margin Methods for Structured and Interdependent Output Variables with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Large Margin Methods for Structured and Interdependent Output Variables more than expected).
Fields of papers citing Large Margin Methods for Structured and Interdependent Output Variables
This network shows the impact of Large Margin Methods for Structured and Interdependent Output Variables. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Large Margin Methods for Structured and Interdependent Output Variables.
About Large Margin Methods for Structured and Interdependent Output Variables
This paper, published in 2005, received 1.2k indexed citations . Written by Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann and Yasemin Altün covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (762 citations), Computer Vision and Pattern Recognition (592 citations) and Molecular Biology (135 citations). Published in Journal of Machine Learning Research.
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/w5479647.