John Stutz
- Signal Processing top 5%
- Artificial Intelligence top 2%
- Bayesian Methods and Mixture Models 2
- Bayesian Modeling and Causal Inference 1
- Information Systems top 5%
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- Advanced Statistical Methods and Models 3
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- Risk and Safety Analysis 2
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- Spectroscopy and Chemometric Analyses 2
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- 3D Shape Modeling and Analysis 1
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- Innovations in Concrete and Construction Materials 1
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- Planetary Science and Exploration 1
- Co-authors
- Peter CheesemanRobin HansonJeremy FrankJ. CastleNikunj C. OzaJ. H. GoebelMatthew W. SelfH. J. Walker
- Journals
- Proceedings of the American Mathematical Society (1 paper)Journal of Artificial Intelligence Research (1 paper)Transactions of the American Mathematical Society (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
John Stutz
14 papers receiving 785 citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Signal Processing 204
- Artificial Intelligence 539
- Information Systems 246
- Computational Theory and Mathematics 95
- Computer Networks and Communications 130
Countries citing papers authored by John Stutz
This map shows the geographic impact of John Stutz'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 John Stutz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Stutz more than expected).
Fields of papers citing papers by John Stutz
This network shows the impact of papers produced by John Stutz. 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 John Stutz. The network helps show where John Stutz may publish in the future.
Co-authorship network
The 16 scholars most cited alongside John Stutz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 1 | |
| 2 | 2009 | 33 | |
| 3 | 2007 | 5 | |
| 4 | 2005 | 7 | |
| 5 | 1997 | 57 | |
| 6 | Bayesian classification (AutoClass): theory and resultsbreakdown → | 1996 | 666 |
| 7 | Subpixel Resolution from Multiple Images | 1994 | 3 |
| 8 | An Improved Automatic Classification of a Landsat/TM Image from Kansas (FIFE) | 1994 | 5 |
| 9 | Bayesian Classification Scheme | 1992 | 0 |
| 10 | Bayesian classification with correlation and inheritance | 1991 | 30 |
| 11 | Bayesian classification theory | 1991 | 78 |
| 12 | Automatic classification of spectra from the Infrared Astronomical Satellite (IRAS) | 1989 | 17 |
| 13 | A Bayesian classification of the IRAS LRS Atlas | 1989 | 10 |
| 14 | Automatic discovery of optimal classes | 1986 | 1 |
| 15 | 1976 | 0 | |
| 16 | 1972 | 9 |
About John Stutz
John Stutz is a scholar working on Algebra and Number Theory, Statistics and Probability and Computer Graphics and Computer-Aided Design, having authored 16 papers that have together received 922 indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (3 papers), Bayesian Methods and Mixture Models (2 papers), Risk and Safety Analysis (2 papers), Spectroscopy and Chemometric Analyses (2 papers), 3D Shape Modeling and Analysis (1 paper), Innovations in Concrete and Construction Materials (1 paper), Planetary Science and Exploration (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Signal Processing (204 citations), Artificial Intelligence (539 citations) and Information Systems (246 citations). John Stutz has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Peter Cheeseman, Robin Hanson, Jeremy Frank, J. Castle, Nikunj C. Oza, J. H. Goebel, Matthew W. Self, H. J. Walker, Kevin Volk and A. Jalobeanu. Their work appears in journals such as Proceedings of the American Mathematical Society, Journal of Artificial Intelligence Research, Transactions of the American Mathematical Society, Knowledge Discovery and Data Mining and NASA Technical Reports Server (NASA).
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.