Ben Hamner
Impact in
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- Emotion and Mood Recognition
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- Face recognition and analysis
- Face and Expression Recognition
- Advanced Neural Network Applications
Papers in
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- Data Quality and Management 2
- Psychometric Methodologies and Testing 2
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- Educational Technology and Assessment 3
- Co-authors
- Mark D. Shermis (3 shared papers)Pat Jangyodsuk (2 shared papers)Vassilis Athitsos (2 shared papers)Hugo Jair Escalante (2 shared papers)Isabelle Guyon (2 shared papers)R. Leeb (1 shared paper)Michele Tavella (1 shared paper)José del R. Millán (1 shared paper)
- Journals
- Neural Networks (1 paper)Drug Discovery Today (1 paper)Astronomy and Computing (1 paper)Lecture notes in computer science (2 papers)IEEE International Conference on Big Data (BigData) (1 paper)
- Partner nations
- United StatesUnited KingdomMexico
In The Last Decade
Ben Hamner
13 papers receiving 1.9k citations
Ben Hamner's Hit Papers
Peers
Comparison fields: 5 of 84
- Experimental and Cognitive Psychology 1.3k
- Computer Vision and Pattern Recognition 475
- Human-Computer Interaction 121
- Computer Science Applications 50
- Artificial Intelligence 227
Countries citing papers authored by Ben Hamner
This map shows the geographic impact of Ben Hamner'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 Ben Hamner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben Hamner more than expected).
Fields of papers citing papers by Ben Hamner
This network shows the impact of papers produced by Ben Hamner. 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 Ben Hamner. The network helps show where Ben Hamner may publish in the future.
Co-authors
The 17 scholars most cited alongside Ben Hamner, 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 | Challenges in Representation Learning: A Report on Three Machine Learning Contests Hit paper breakdown → | 2013 | 1149 |
| 2 | Challenges in representation learning: A report on three machine learning contests Hit paper breakdown → | 2014 | 502 |
| 3 | 2012 | 73 | |
| 4 | 2013 | 47 | |
| 5 | 2015 | 37 | |
| 6 | 2013 | 33 | |
| 7 | 2011 | 27 | |
| 8 | 2013 | 21 | |
| 9 | 2013 | 21 | |
| 10 | Evaluation Metrics for Machine Learning [R package Metrics version 0.1.4] | 2018 | 16 |
| 11 | 2013 | 5 | |
| 12 | 2014 | 4 | |
| 13 | 2013 | 3 |
About Ben Hamner
Ben Hamner is a scholar working on Management Science and Operations Research, Information Systems, Human-Computer Interaction, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 13 papers that have together received 1.9k indexed citations. Recurring topics across this work include Educational Technology and Assessment (3 papers), Data Quality and Management (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Psychometric Methodologies and Testing (2 papers), Machine Learning and Data Classification (2 papers), Hand Gesture Recognition Systems (2 papers), Machine Learning and Algorithms (2 papers) and scientometrics and bibliometrics research (2 papers). The work is most often cited by research in Experimental and Cognitive Psychology (1.3k citations), Computer Vision and Pattern Recognition (475 citations), Human-Computer Interaction (121 citations), Computer Science Applications (50 citations) and Artificial Intelligence (227 citations). Ben Hamner has collaborated with scholars based in United States, United Kingdom and Mexico. Frequent co-authors include Mark D. Shermis, Pat Jangyodsuk, Vassilis Athitsos, Hugo Jair Escalante, Isabelle Guyon, R. Leeb, Michele Tavella, José del R. Millán, Martine De Cock and David C. Thompson. Their work appears in journals such as Neural Networks, Drug Discovery Today, Astronomy and Computing, Lecture notes in computer science and IEEE International Conference on Big Data (BigData).
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.