Thomas Zeugmann

1.8k total citations
60 papers, 538 citations indexed

About

Thomas Zeugmann is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Thomas Zeugmann has authored 60 papers receiving a total of 538 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 32 papers in Computational Theory and Mathematics and 8 papers in Computer Networks and Communications. Recurrent topics in Thomas Zeugmann's work include Machine Learning and Algorithms (37 papers), Algorithms and Data Compression (21 papers) and Computability, Logic, AI Algorithms (20 papers). Thomas Zeugmann is often cited by papers focused on Machine Learning and Algorithms (37 papers), Algorithms and Data Compression (21 papers) and Computability, Logic, AI Algorithms (20 papers). Thomas Zeugmann collaborates with scholars based in Japan, Germany and United States. Thomas Zeugmann's co-authors include Steffen Lange, Sandra Zilles, Rolf Wiehagen, Efim Kinber, Osamu Watanabe, Sanjay Jain, Peter Rossmanith, John Case, Ricard Gavaldà and Gábor Lugosi and has published in prestigious journals such as Machine Learning, Theoretical Computer Science and Journal of Computer and System Sciences.

In The Last Decade

Thomas Zeugmann

54 papers receiving 519 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Thomas Zeugmann Japan 15 491 368 52 18 16 60 538
Linda Sellie United States 7 487 1.0× 227 0.6× 73 1.4× 32 1.8× 11 0.7× 11 529
Sam Buss United States 13 295 0.6× 392 1.1× 51 1.0× 9 0.5× 4 0.3× 35 483
Sven Schewe United Kingdom 10 220 0.4× 249 0.7× 42 0.8× 19 1.1× 13 0.8× 66 360
Gerald E. Peterson United States 7 216 0.4× 157 0.4× 19 0.4× 11 0.6× 13 0.8× 21 310
Walter S. Brainerd United States 7 204 0.4× 181 0.5× 43 0.8× 4 0.2× 5 0.3× 14 309
Enea Zaffanella Italy 9 205 0.4× 275 0.7× 36 0.7× 6 0.3× 11 0.7× 31 364
Tudor Jebelean Austria 11 279 0.6× 231 0.6× 42 0.8× 3 0.2× 6 0.4× 46 337
Daniel Dadush Netherlands 10 92 0.2× 160 0.4× 35 0.7× 12 0.7× 13 0.8× 37 282
Abdullah Al-Dujaili Singapore 9 174 0.4× 57 0.2× 83 1.6× 21 1.2× 9 0.6× 20 239
Diederick Vermetten Netherlands 9 213 0.4× 130 0.4× 18 0.3× 44 2.4× 4 0.3× 40 285

Countries citing papers authored by Thomas Zeugmann

Since Specialization
Citations

This map shows the geographic impact of Thomas Zeugmann'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 Zeugmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Zeugmann more than expected).

Fields of papers citing papers by Thomas Zeugmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Zeugmann. 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 Zeugmann. The network helps show where Thomas Zeugmann may publish in the future.

Co-authorship network of co-authors of Thomas Zeugmann

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Zeugmann. A scholar is included among the top collaborators of Thomas Zeugmann 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 Zeugmann. Thomas Zeugmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Auer, Peter, Alexander Clark, & Thomas Zeugmann. (2016). Guest editors' foreword. Theoretical Computer Science. 650. 1–3. 1 indexed citations
2.
Jordan, Charles & Thomas Zeugmann. (2012). Testable and untestable classes of first-order formulae. Journal of Computer and System Sciences. 78(5). 1557–1578.
3.
Stephan, Frank, Ryo Yoshinaka, & Thomas Zeugmann. (2011). On the Parameterised Complexity of Learning Patterns.. 277–281. 3 indexed citations
4.
Balbach, Frank J. & Thomas Zeugmann. (2010). Teaching randomized learners with feedback. Information and Computation. 209(3). 296–319.
5.
Watanabe, Osamu & Thomas Zeugmann. (2009). Stochastic algorithms : foundations and applications : 5th international symposium, SAGA 2009 Sapporo, Japan, October 26-28, 2009 : proceedings. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 20 indexed citations
6.
Gavaldà, Ricard, Gábor Lugosi, Thomas Zeugmann, & Sandra Zilles. (2009). Algorithmic learning theory : 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009 : proceedings. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 1 indexed citations
7.
Zeugmann, Thomas, et al.. (2008). Consistent and coherent learning with δ-delay. Information and Computation. 206(11). 1362–1374. 4 indexed citations
8.
Zeugmann, Thomas & Sandra Zilles. (2008). Learning recursive functions: A survey. Theoretical Computer Science. 397(1-3). 4–56. 19 indexed citations
9.
Lange, Steffen, Thomas Zeugmann, & Sandra Zilles. (2008). Learning indexed families of recursive languages from positive data: A survey. Theoretical Computer Science. 397(1-3). 194–232. 26 indexed citations
10.
Jain, Sanjay, Efim Kinber, Rolf Wiehagen, & Thomas Zeugmann. (2003). On learning of functions refutably. Theoretical Computer Science. 298(1). 111–143. 3 indexed citations
11.
Stephan, Frank & Thomas Zeugmann. (2002). Learning classes of approximations to non-recursive functions. Theoretical Computer Science. 288(2). 309–341. 2 indexed citations
12.
Abe, Naoki, Roni Khardon, & Thomas Zeugmann. (2001). Proceedings of the 12th International Conference on Algorithmic Learning Theory. 6 indexed citations
13.
Erlebach, Thomas, et al.. (2001). Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries. Theoretical Computer Science. 261(1). 119–156. 15 indexed citations
14.
Jain, Sanjay, Efim Kinber, Steffen Lange, Rolf Wiehagen, & Thomas Zeugmann. (2000). Learning languages and functions by erasing. Theoretical Computer Science. 241(1-2). 143–189. 2 indexed citations
15.
Case, John, Sanjay Jain, Steffen Lange, & Thomas Zeugmann. (1999). Incremental Concept Learning for Bounded Data Mining. Information and Computation. 152(1). 74–110. 32 indexed citations
16.
Lange, Steffen & Thomas Zeugmann. (1996). Incremental Learning from Positive Data. Journal of Computer and System Sciences. 53(1). 88–103. 42 indexed citations
17.
Wiehagen, Rolf, Carl H. Smith, & Thomas Zeugmann. (1994). Classification of predicates and languages. 171–181. 2 indexed citations
18.
Zeugmann, Thomas. (1989). Improved parallel computations in the ring Z / p a. Journal of automata, languages and combinatorics. 25(10). 543–547. 2 indexed citations
19.
Kinber, Efim & Thomas Zeugmann. (1985). Inductive Inference of Almost Everywhere Correct Programs by Reliably Working Strategies.. Journal of automata, languages and combinatorics. 21(45). 91–100. 9 indexed citations
20.
Zeugmann, Thomas. (1983). On the Synthesis of Fastest Programs in Inductive Inference.. Journal of automata, languages and combinatorics. 19. 625–642. 2 indexed citations

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.

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