Using the Nyström Method to Speed Up Kernel Machines

905 indexed citations

Abstract

loading...

About

This paper, published in 2000, received 905 indexed citations. Written by Christopher K. I. Williams and Matthias Seeger covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (591 citations), Computer Vision and Pattern Recognition (443 citations) and Computational Mechanics (231 citations). Published in Neural Information Processing Systems.

In The Last Decade

doi.org/w9942987 →

Countries where authors are citing Using the Nyström Method to Speed Up Kernel Machines

Specialization
Citations

This map shows the geographic impact of Using the Nyström Method to Speed Up Kernel 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 Using the Nyström Method to Speed Up Kernel Machines with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Using the Nyström Method to Speed Up Kernel Machines more than expected).

Fields of papers citing Using the Nyström Method to Speed Up Kernel Machines

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Using the Nyström Method to Speed Up Kernel Machines. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Using the Nyström Method to Speed Up Kernel 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/w9942987.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026