Thomas R. Hancock

476 total citations
24 papers, 264 citations indexed

About

Thomas R. Hancock is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Thomas R. Hancock has authored 24 papers receiving a total of 264 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 9 papers in Computational Theory and Mathematics and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Thomas R. Hancock's work include Machine Learning and Algorithms (16 papers), Machine Learning and Data Classification (8 papers) and Neural Networks and Applications (6 papers). Thomas R. Hancock is often cited by papers focused on Machine Learning and Algorithms (16 papers), Machine Learning and Data Classification (8 papers) and Neural Networks and Applications (6 papers). Thomas R. Hancock collaborates with scholars based in United States, Canada and Germany. Thomas R. Hancock's 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 and has published in prestigious journals such as The Astronomical Journal, Neural Networks and Machine Learning.

In The Last Decade

Thomas R. Hancock

23 papers receiving 238 citations

Peers

Countries citing papers authored by Thomas R. Hancock

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Automatic vehicle location using sensory maps: a brief demonstration 2002 ·Thomas R. Hancock, (unknown), (unknown), Scott Rickard, (unknown)	1
2	Lower Bounds on Learning Decision Lists and Trees 1996 ·Information and Computation ·Thomas R. Hancock, Tao Jiang, (unknown), John Tromp	48
3	Asking Questions to Minimize Errors 1996 ·Journal of Computer and System Sciences ·Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock, (unknown)	12
4	On learning μ-perceptron networks on the uniform distribution 1996 ·Neural Networks ·Mostefa Golea, Mario Marchand, Thomas R. Hancock	4
5	Learning Boolean Read-Once Formulas over Generalized Bases 1995 ·Journal of Computer and System Sciences ·Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein	23
6	Learning Arithmetic Read-Once Formulas 1995 ·SIAM Journal on Computing ·Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein	18
7	Exploiting the Absence of Irrelevant Information: What You Don't Know Can Help You 1994 ·R. Bharat Rao, Russell Greiner, Thomas R. Hancock	1
8	Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries 1994 ·Machine Learning ·Thomas R. Hancock, Mostefa Golea, Mario Marchand	8
9	Learning nonoverlapping perceptron networks from examples and membership queries 1994 ·Machine Learning ·Thomas R. Hancock, Mostefa Golea, Mario Marchand	5
10	Asking questions to minimize errors 1993 ·Open Scholarship Institutional Repository (Washington University in St. Louis) ·Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock, (unknown)	25
11	Learning kμ decision trees on the uniform distribution 1993 ·Thomas R. Hancock	6
12	Infrared imaging of M CAS B using rapid image motion compensation 1993 ·The Astronomical Journal ·(unknown), Thomas R. Hancock, (unknown), B. A. McLeod, Michael Lloyd‐Hart, D. Colucci, Peter Wizinowich, Mark Clampin, D. A. Golimowski, S. T. Durrance	5
13	On Learning µ-Perceptron Networks with Binary Weights 1992 ·Neural Information Processing Systems ·Mostefa Golea, Mario Marchand, Thomas R. Hancock	7
14	The complexity of learning formulas and decision trees that have restricted reads 1992 ·Thomas R. Hancock	12
15	Learning Boolean read-once formulas with arbitrary symmetric and constant fan-in gates 1992 ·Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein	23
16	Learning arithmetic read-once formulas 1992 ·Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein	16
17	Learning 2µ DNF Formulas and kµ Decision Trees. 1991 ·Conference on Learning Theory ·Thomas R. Hancock	3
18	Learning 2u DNF formulas and ku decision trees 1991 ·Conference on Learning Theory ·Thomas R. Hancock	21
19	Learning 2μ-DNF formulas and κμ decision trees 1991 ·Medical Entomology and Zoology ·Thomas R. Hancock	1
20	Read-Once Threshold Formulas, Justifying Assignments, and Generic Transformations 1991 ·Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein, Marek Karpiński	5

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.

Explore authors with similar magnitude of impact