Thomas R. Hancock
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Co-authors
- Nader H. BshoutyLisa HellersteinTao JiangJohn TrompSally A. GoldmanMario MarchandMostefa GoleaMarek Karpiński
- Topics
- Machine Learning and Algorithms (16 papers)Machine Learning and Data Classification (8 papers)Neural Networks and Applications (6 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceComputer Networks and Communications
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Thomas R. Hancock
23 papers receiving 238 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 235
- Computational Theory and Mathematics 123
- Computer Networks and Communications 35
- Computer Vision and Pattern Recognition 15
- Electrical and Electronic Engineering 14
Countries citing papers authored by Thomas R. Hancock
This map shows the geographic impact of Thomas R. Hancock'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 Thomas R. Hancock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas R. Hancock more than expected).
Fields of papers citing papers by Thomas R. Hancock
This network shows the impact of papers produced by Thomas R. Hancock. 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 Thomas R. Hancock. The network helps show where Thomas R. Hancock may publish in the future.
Co-authorship network of co-authors of Thomas R. Hancock
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas R. Hancock. A scholar is included among the top collaborators of Thomas R. Hancock 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 Thomas R. Hancock. Thomas R. Hancock is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 48 | |
| 3 | 12 | |
| 4 | 4 | |
| 5 | 23 | |
| 6 | 18 | |
| 7 | Exploiting the Absence of Irrelevant Information: What You Don't Know Can Help You | 1 |
| 8 | 8 | |
| 9 | 5 | |
| 10 | 25 | |
| 11 | 6 | |
| 12 | 5 | |
| 13 | On Learning µ-Perceptron Networks with Binary Weights | 7 |
| 14 | The complexity of learning formulas and decision trees that have restricted reads | 12 |
| 15 | 23 | |
| 16 | 16 | |
| 17 | Learning 2µ DNF Formulas and kµ Decision Trees. | 3 |
| 18 | 21 | |
| 19 | Learning 2μ-DNF formulas and κμ decision trees | 1 |
| 20 | Read-Once Threshold Formulas, Justifying Assignments, and Generic Transformations | 5 |
About Thomas R. Hancock
Thomas R. Hancock is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 24 papers that have together received 264 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (16 papers), Machine Learning and Data Classification (8 papers) and Neural Networks and Applications (6 papers). The work is most often cited by research in Computational Theory and Mathematics (123 citations), Artificial Intelligence (235 citations) and Computer Networks and Communications (35 citations). Thomas R. Hancock has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Nader H. Bshouty, Lisa Hellerstein, Tao Jiang, John Tromp, Sally A. Goldman, Mario Marchand, Mostefa Golea, Marek Karpiński, Long-Ji Lin and D. Colucci. Their work appears in journals such as The Astronomical Journal, Neural Networks and Machine Learning.
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.