On the algorithmic implementation of multiclass kernel-based vector machines

1.2k indexed citations

Abstract

loading...

About

This paper, published in 2002, received 1.2k indexed citations. Written by Koby Crammer and Yoram Singer covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (734 citations), Computer Vision and Pattern Recognition (514 citations) and Molecular Biology (128 citations). Published in Journal of Machine Learning Research.

In The Last Decade

doi.org/w19492382 →

Countries where authors are citing On the algorithmic implementation of multiclass kernel-based vector machines

Specialization
Citations

This map shows the geographic impact of On the algorithmic implementation of multiclass kernel-based 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 On the algorithmic implementation of multiclass kernel-based 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 On the algorithmic implementation of multiclass kernel-based vector machines more than expected).

Fields of papers citing On the algorithmic implementation of multiclass kernel-based vector machines

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of On the algorithmic implementation of multiclass kernel-based 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 On the algorithmic implementation of multiclass kernel-based vector machines.

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/w19492382.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026