Algorithmic Learning in a Random World
- Authors
- Vladimir VovkGlenn Shafer
- Journal
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B))
In The Last Decade
doi.org/10.1007/b106715 →Countries where authors are citing Algorithmic Learning in a Random World
This map shows the geographic impact of Algorithmic Learning in a Random World. 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 Algorithmic Learning in a Random World with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Algorithmic Learning in a Random World more than expected).
Fields of papers citing Algorithmic Learning in a Random World
This network shows the impact of Algorithmic Learning in a Random World. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Algorithmic Learning in a Random World.
About Algorithmic Learning in a Random World
This paper, published in 2005, received 575 indexed citations . Written by Vladimir Vovk and Glenn Shafer covering the research area of Computational Theory and Mathematics. It is primarily cited by scholars working on Artificial Intelligence (300 citations), Statistics and Probability (87 citations) and Computational Theory and Mathematics (82 citations). Published in Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)).
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/10.1007/b106715.