Michael Frazier
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Computer Networks and Communications
- Information Systems
- Management Science and Operations Research
- Topics
- Machine Learning and Algorithms (10 papers)semigroups and automata theory (6 papers)Algorithms and Data Compression (6 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceComputer Networks and Communications
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Michael Frazier
13 papers receiving 193 citations
Peers
Comparison fields: 5 of 20
- Artificial Intelligence 201
- Computational Theory and Mathematics 115
- Computer Networks and Communications 41
- Information Systems 14
- Management Science and Operations Research 9
Countries citing papers authored by Michael Frazier
This map shows the geographic impact of Michael Frazier's research. 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 Michael Frazier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Frazier more than expected).
Fields of papers citing papers by Michael Frazier
This network shows the impact of papers produced by Michael Frazier. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Frazier. The network helps show where Michael Frazier may publish in the future.
Co-authorship network of co-authors of Michael Frazier
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Frazier. A scholar is included among the top collaborators of Michael Frazier based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Frazier. Michael Frazier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Introducing computer science after programming | 5 |
| 2 | 4 | |
| 3 | 16 | |
| 4 | 9 | |
| 5 | 10 | |
| 6 | Matters horn and other features in the computational learning theory landscape: the notion of membership | 4 |
| 7 | 17 | |
| 8 | 5 | |
| 9 | 25 | |
| 10 | Learnability in inductive logic programming: some basic results and techniques | 8 |
| 11 | 100 | |
| 12 | 6 | |
| 13 | 1 |
About Michael Frazier
Michael Frazier is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Computer Science Applications, having authored 13 papers that have together received 210 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (10 papers), semigroups and automata theory (6 papers) and Algorithms and Data Compression (6 papers). The work is most often cited by research in Computational Theory and Mathematics (115 citations), Artificial Intelligence (201 citations) and Computer Networks and Communications (41 citations). Michael Frazier has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Leonard Pitt, Dana Angluin, Sally A. Goldman, Nina Mishra and C. David Page. Their work appears in journals such as Machine Learning, Journal of Computer and System Sciences and Information Processing Letters.
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.