1. Thomas Zeugmann, On Recursive Optimizers, in “Mathematical Methods of Specification and Synthesis of Software Systems '85, Proceedings of the International Spring School Wendisch-Rietz, GDR, April 22-26, 1985,” Lecture Notes in Computer Science 215, pp. 240-245, Springer-Verlag 1986.
   Abstract.
2. Thomas Zeugmann, On Barzdin's Conjecture, in “Analogical and Inductive Inference, International Workshop AII '86. Wendisch-Rietz, GDR, October 1986, Proceedings,” Lecture Notes in Computer Science 265, pp. 220-227, Springer-Verlag 1986.
3. Efim Kinber and Thomas Zeugmann, Monte-Carlo inference and its relations to reliable frequency identification, in “Fundamentals of Computation Theory, International Conference FCT '89 Szeged, Hungary, August 21-25, 1989, Proceedings,” (J. Csirik, J. Demetrovics, and F. Gécseg, Eds.), Lecture Notes in Computer Science 380, pp. 257-266, Springer-Verlag 1989.
   Abstract.
4. Efim B. Kinber and Thomas Zeugmann, Refined Query Inference, in “Analogical and Inductive Inference, International Workshop All '89 Reinhardsbrunn Castle, GDR, October 1-6, 1989, Proceedings,” (K.P. Jantke Ed.), Lecture Notes in Artificial Intelligence 397, pp. 148 - 160, Springer-Verlag 1989.
   Abstract
5. Efim B. Kinber, William I. Gasarch, Thomas Zeugmann, Mark G. Pleszkoch, and Carl H. Smith, Learning via Queries with Teams and Anomalies, in “COLT '90, Proc. Third Annual Workshop on Computational Learning Theory,” (Mark A. Fulk and John Case, Eds.), pp. 327 - 337, Morgan Kaufmann Publishers Inc., 1990.
6. Thomas Zeugmann, Computing Large Polynomial Powers Very Fast in Parallel, in “Mathematical Foundations of Computer Science 1990, Banská Bystrica, Czechoslovakia, August 1990, Proceedings,” B. Rovan (Ed.), Lecture Notes in Computer Science 452, pp. 538 - 544, Springer-Verlag 1990.
   Abstract
7. Thomas Zeugmann, Inductive Inference of Optimal Programs: A Survey and Open Problems, in “Nonmonotonic and Inductive Logic, 1st International Workshop, Karlsruhe, Germany, December 1990, Proceedings,” (J. Dix, K.P. Jantke, and P.H. Schmitt, Eds.), Lecture Notes in Artificial Intelligence 543, pp. 208-222, Springer-Verlag 1991.
   Abstract
8. Steffen Lange and Thomas Zeugmann, Types of Monotonic Language Learning and Their Characterization, in “Proc. 5th Annual ACM Workshop on Computational Learning Theory,” Pittsburgh, July 27th - 29th, 1992, pp. 377 - 390, ACM Press 1992.
   Abstract
9. Rolf Wiehagen and Thomas Zeugmann, Too Much Information Can be too Much for Efficient Learning, (invited paper), in “Analogical and Inductive Inference, AII '92, Dagstuhl Castle, Germany, October 1992, Proceedings,” (K.P. Jantke, ed.), Lecture Notes in Artificial Intelligence 642, pp. 72 - 86, Springer-Verlag 1992.
   Abstract
10. Steffen Lange and Thomas Zeugmann, A Unifying Approach to Monotonic Language Learning on Informant, in “Analogical and Inductive Inference, AII '92, Dagstuhl Castle, Germany, October 1992, Proceedings,” (K.P. Jantke, ed.), Lecture Notes in Artificial Intelligence 642, pp. 244 - 259, Springer-Verlag 1992.
    Abstract
11. Steffen Lange and Thomas Zeugmann, Monotonic Versus Non-monotonic Language Learning, in “Nonmonotonic and Inductive Logic, Second International Workshop, Reinhardsbrunn Castle, Germany, December 1991,” (G. Brewka, K.P. Jantke, and P.H. Schmitt, Eds.), Lecture Notes in Artificial Intelligence 659 pp. 254 - 269, Springer-Verlag 1993.
    Abstract
12. Steffen Lange and Thomas Zeugmann, Language Learning with a Bounded Number of Mind Changes, in “STACS 93, 10th Annual Symposium on Theoretical Ascpects of Computer Science, Würzburg, Germany, February 1993, Proceedings,” (P. Enjalbert, A. Finkel, and K.W. Wagner, Eds.), Lecture Notes in Computer Science 665, pp. 682 - 691, Springer-Verlag 1993.
    Abstract
13. Steffen Lange and Thomas Zeugmann, Language Learning in Dependence on the Space of Hypotheses, in “Proc. 6th Annual ACM Conference on Computational Learning Theory,” July 26th - 28th, 1993, Santa Cruz, pp. 127 - 136, ACM Press 1993.
    Abstract
14. Rolf Wiehagen, Carl H. Smith, and Thomas Zeugmann, Classification of Predicates and Languages, in “Proc. 1st EURO-COLT'93 - Computational Learning Theory,” 20th - 22nd December, 1993, University of London, The Institute of Mathematics and its Applications Conference Series, New Series Number 53, pp. 171 - 181, Oxford University Press, Oxford 1994.
    Abstract
15. S. Lange and T. Zeugmann, Set-driven and Rearrangement-Independent Learning of Recursive Languages, in “Algorithmic Learning Theory, 4th International Workshop on Analogical and Inductive Inference, AII '94, 5th International Workshop on Algorithmic Learning Theory, ALT '94, Reinhardsbrunn Castle, Germany, October 1994, Proceedings,” (Setsuo Arikawa and Klaus P. Jantke, Eds.), Lecture Notes in Artificial Intelligence 872, pp. 453 - 468, Springer-Verlag 1994.
    Abstract
16. Steffen Lange and Thomas Zeugmann, Trading Monotonicity Demands versus Mind Changes, in “Computational Learning Theory, Second European Conference, EuroCOLT '95, Barcelona, Spain, March 1995, Proceedings,” (P. Vitanyi, Ed.), Lecture Notes in Artificial Intelligence 904, pp. 125 - 139, Springer 1995.
    Abstract
17. Steffen Lange and Thomas Zeugmann, Refined Incremental Learning, in “Proc. 8th Australian Joint Conference on Artificial Intelligence - AI'95,” (Xin Yao, Ed.), pp. 147 - 154, World Scientific Publ. Co., 1995.
    Abstract
18. Rusins Freivalds and Thomas Zeugmann, Co-Learning of Recursive Languages from Positive Data, in “Perspectives of System Informatics, Second International Andrei Ershov Memorial Conference, Akademgorodok, Novosibirsk, Russia, June 1996, Proceedings,” (D. Bjørner, M. Broy, and I. Pottosin, Eds.), Lecture Notes in Computer Science 1181, pp. 122 - 133, Springer 1996.
    Abstract
19. S. Lange, R. Wiehagen, and T. Zeugmann, Learning by Erasing, in “Algorithmic Learning Theory, 7th International Workshop, ALT '96, Sydney, Australia, October 1996, Proceedings,” (Setsuo Arikawa and Arun K. Sharma, Eds.), Lecture Notes in Artificial Intelligence 1160, pp. 228 - 241, Springer 1996.
    Abstract
20. J. Case, S. Jain, S. Lange, and T. Zeugmann, Learning Concepts Incrementally with Bounded Data Mining, “Proc. Automata Induction, Grammatical Inference, and Language Acquisition,” Workshop at the 14th International Conference on Machine Learning (ICML-97), Nashville, Tennessee, July 12, 1997.
    Abstract.
21. T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger, and T. Zeugmann, Learning One-Variable Pattern Languages Very Efficiently on Average, in Parallel, and by Asking Queries, in “Algorithmic Learning Theory, 8th International Workshop, ALT '97, Sendai, Japan, October 1997, Proceedings,” (M. Li and A. Maruoka, Eds.), Lecture Notes in Artificial Intelligence 1316, pp. 260 - 276, Springer 1997.
    Abstract
22. P. Rossmanith and T. Zeugmann, Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data, in “Grammatical Inference, 4th International Colloquium, ICGI-98, Ames, Iowa, USA, July 1998, Proceedings,” (V. Honavar and G. Slutzki, Eds.), Lecture Notes in Artificial Intelligence 1433, pp. 13 - 24, Springer 1998.
    Abstract.
23. R. Reischuk and T. Zeugmann, Learning One-Variable Pattern Languages in Linear Average Time, in “Proc. 11th Annual Conference on Computational Learning Theory - COLT'98,” July 24th - 26th, Madison, pp. 198 - 208, ACM Press 1998.
    Abstract.
24. Rüdiger Reischuk and Thomas Zeugmann, A Complete and Tight Average-Case Analysis of Learning Monomials in “STACS'99, 16th Annual Symposium on Theoretical Aspects of Computer Science, Trier, Germany, March 1999, Proceedings,” (C. Meinel and S. Tison, Eds.), Lecture Notes in Computer Science 1563, pp. 414 - 423, Springer 1999.
    Abstract.
25. Frank Stephan and Thomas Zeugmann, On the Uniform Learnability of Approximations to Non-Recursive Functions, in “Algorithmic Learning Theory, 10th International Conference, ALT'99, Tokyo, Japan, December 1999, Proceedings,” (Osamu Watanabe and Takashi Yokomori, Eds.), Lecture Notes in Artificial Intelligence 1720, pp. 276 - 290, Springer 1999.
    Abstract.
26. Frank Stephan and Thomas Zeugmann, Average-Case Complexity of Learning Polynomials, in “Proc. 13th Annual Conference on Computational Learning Theory,” June 28th - July 1st, Stanford University, Palo Alto, (N. Cesa-Bianchi and S. Goldman, Eds.) pp. 59 - 68, Morgan Kaufmann Publ. 2000.
    Abstract.
27. G. Grieser, S. Lange, and T. Zeugmann, Learning Recursive Concepts with Anomalies, in “Algorithmic Learning Theory, 11th International Conference, ALT 2000, Sydney, Australia, December 2000, Proceedings,” (Hiroki Arimura, Sanjay Jain and Arun Sharma, Eds.), Lecture Notes in Artificial Intelligence 1968, pp. 101 - 115, Springer 2000.
    Abstract.
28. Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas Zeugmann, Learning Recursive Functions Refutably, in “Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings,” (Naoki Abe, Roni Khardon and Thomas Zeugmann, Eds.), Lecture Notes in Artificial Intelligence 2225, pp. 283 - 298, Springer 2001.
    Abstract.
29. Thomas Zeugmann, Stochastic Finite Learning, (invited paper), in “Stochastic Algorithms: Foundations and Applications, International Symposium SAGA 2001, Berlin, Germany, December 13-14, 2001, Proceedings,” (Kathleen Steinhöfel, Ed.), Lecture Notes in Computer Science 2264, pp. 155 - 171, Springer 2001.
    Abstract.
30. Thomas Zeugmann, Can Learning in the Limit Be Done Efficiently?, (invited paper), in “Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 17 - 19, 2003, Proceedings,” (Ricard Gavaldà, Klaus P. Jantke and Eiji Takimoto, Eds.), Lecture Notes in Artificial Intelligence 2842, pp. 17 - 38, Springer 2003.
    Abstract.
31. John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan and Thomas Zeugmann, Learning a Subclass of Regular Patterns in Polynomial Time, in “Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 17 - 19, 2003, Proceedings,” (Ricard Gavaldà, Klaus P. Jantke and Eiji Takimoto, Eds.), Lecture Notes in Artificial Intelligence 2842, pp. 234 - 246, Springer 2003.
    Abstract.
32. Frank J. Balbach and Thomas Zeugmann, Teaching Learners with Restricted Mind Changes, in “Algorithmic Learning Theory, 16th International Conference, ALT 2005, Singapore, October 2005, Proceedings,” (Sanjay Jain, Hans Ulrich Simon and Etsuji Tomita, Eds.), Lecture Notes in Artificial Intelligence 3734, pp. 474 - 489, Springer 2005.
    Abstract.
33. Thomas Zeugmann, Inductive Inference and Language Learning, in “Theory and Applications of Models of Computation, Third International Conference, TAMC 2006, Beijing, China, May 2006, Proceedings,”, (Jin-Yi Cai, S. Barry Cooper and Angsheng Li, Eds.), Lecture Notes in Computer Science 3959, pp. 464 - 473, Springer 2006.
    Abstract.
34. Frank J. Balbach and Thomas Zeugmann, Teaching Randomized Learners, in “Learning Theory, 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 2006, Proceedings,” (Gabor Lugosi and Hans Ulrich Simon, Eds.), Lecture Notes in Artificial Intelligence 4005, pp. 229 - 243, Springer 2006.
    Abstract.
35. Jan Poland and Thomas Zeugmann, Clustering the Google Distance with Eigenvectors and Semidefinite Programming, in “Knowledge Media Technologies, First International Core-to-Core Workshop,” Dagstuhl, July 23-27, 2006, Germany, (Klaus P. Jantke & Gunther Kreuzberger, Eds.), Diskussionsbeiträge, Institut für Medien und Kommunikationswissenschaft, Technische Universität Ilmenau No. 21, pp. 61 - 69, July 2006.
    Abstract.
36. Frank J. Balbach and Thomas Zeugmann, Teaching Memoryless Randomized Learners Without Feedback, in “Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 2006, Proceedings,” (José L. Balcázar, Phil M. Long and Frank Stephan, Eds.), Lecture Notes in Artificial Intelligence 4264, pp. 93 - 108, Springer 2006.
    Abstract.
37. Jan Poland and Thomas Zeugmann, Clustering Pairwise Distances with Missing Data: Maximum Cuts versus Normalized Cuts, in “Discovery Science, 9th International Conference, DS 2006, Barcelona, Spain, October 2006, Proceedings,” (Ljupco Todorovski, Nada Lavrač and Klaus P. Jantke, Eds.), Lecture Notes in Artificial Intelligence 4265, pp. 197 - 208, Springer 2006.
    Abstract.
38. Haruya Iwasaki, Shin-ichi Minato, and Thomas Zeugmann, A Method of Variable Ordering for Zero-suppressed Binary Decision Diagrams in Data Mining Applications, in “Proceedings of The Third IEEE International Workshop on Databases for Next-Generation Researchers, SWOD 2007,” pp. 85 - 90, 2007, IEEE.
    Abstract.
39. Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann, Unordered N-gram Representation Based on Zero-suppressed BDDs for Text Mining and Classification, in “Proceedings of the 5th Workshop on Learning with Logics and Logics for Learning (LLLL 2007), The World Convention Center Summit, Miyazaki, Japan, June 18-19, 2007,” (Akihiro Yamamoto and Kouichi Hirata, Eds.), pp. 32 - 38, 2007, JSAI.
40. Skip Jordan and Thomas Zeugmann, Indistinguishability and First-Order Logic, in “Theory and Applications of Models of Computation, 5th International Conference, TAMC 2008, Xi'an, China, April 2008, Proceedings” (Manindra Agrawal and Dingzhu Du and Zhenhua Duan and Angsheng Li, Eds.), Lecture Notes in Computer Science 4978, pp. 94-104, Springer 2008.
    Abstract.
41. Frank J. Balbach and Thomas Zeugmann, Recent Developments in Algorithmic Teaching, (invited paper), in “Language and Automata Theory and Applications, Third International Conference, LATA 2009, Tarragona, Spain, April 2-8, 2009, Proceedings” (Adrian Horia Dediu, Armand Mihai Ionescu, and Carlos Martín-Vide, Eds.), Lecture Notes in Computer Science 5457, pp. 1-18, Springer 2009.
    Abstract.
42. Kimihito Ito, Thomas Zeugmann, and Yu Zhu, Clustering the Normalized Compression Distance for Virus Data, in “Proceedings of the Sixth Workshop on Learning with Logics and Logics for Learning (LLLL 2009), Kyodai Kaikan, Kyoto, Japan, June 6-7, 2009,” pp. 56 - 67, 2009.
    Abstract.
43. Charles Jordan and Thomas Zeugmann, Relational Properties Expressible with One Universal Quantifier are Testable, in “Stochastic Algorithms: Foundations and Applications, 5th International Symposium, SAGA 2009, Sapporo, Japan, October 2009, Proceedings,” (Osamu Watanabe and Thomas Zeugmann, Eds.), Lecture Notes in Computer Science 5792, pp. 141-155, Springer 2009.
    Abstract.
44. Charles Jordan and Thomas Zeugmann, Untestable Properties Expressible with Four First-Order Quantifiers, in “Language and Automata Theory and Applications, 4th International Conference, LATA 2010, Trier, Germany, May 24-28, 2010, Proceedings,” (Adrian-Horia Dediu, Henning Fernau, and Carlos Martín-Vide, Eds.), Lecture Notes in Computer Science 6031, pp. 333-343, Springer 2010.
    Abstract.
45. Charles Jordan and Thomas Zeugmann, A Note on the Testability of Ramsey's Class, in “Theory and Applications of Models of Computation,
    7th Annual Conference, TAMC 2010, Prague, Czech Republic, June 7-11, 2010, Proceedings,” (Jan Kratochvíl, Angsheng Li, Jiří Fiala, and Petr Kolman, Eds.), Lecture Notes in Computer Science 6108, pp. 296-307, Springer 2010.
    Abstract.
46. Kimihito Ito, Thomas Zeugmann, and Yu Zhu, Recent Experiences in Parameter-Free Data Mining, (invited paper), in “Computer and Information Science, Proceedings of the 25th International Symposium on Computer and Information Sciences.” (Erol Gelenbe, Ricardo Lent, Georgia Sakellari, Ahmet Sacan and Hakki Toroslu and Adnan Yazici, Eds.), Lecture Notes in Electrical Engineering 62, pp. 365-371, Springer 2010.
    Abstract.
47. Charles Jordan and Thomas Zeugmann, Untestable Properties in the Kahr-Moore-Wang Class, in “Logic, Language, Information and Computation,
    18th International Workshop, WoLLIC 2011, Philadelphia, PA, USA, May 18-21, 2011, Proceedings,” (Lev D. Beklemishev and Ruy de Queiroz, Eds.), Lecture Notes in Computer Science 6642, pp. 176-186, Springer 2011.
    Abstract.
48. Rūsiņš Freivalds and Thomas Zeugmann, On the Amount of Nonconstructivity in Learning Recursive Functions, in “Theory and Applications of Models of Computation, 8th Annual Conference, TAMC 2011, Tokyo, Japan, May 23-25, 2011, Proceedings,” (Mitsunori Ogihara and Jun Tarui, Eds.), Lecture Notes in Computer Science 6648, pp. 332-343, Springer 2011.
    Abstract.
49. Frank Stephan, Ryo Yoshinaka, and Thomas Zeugmann, On the Parameterised Complexity of Learning Patterns, in “Computer and Information Sciences II, 26th International Symposium on Computer and Information Sciences,” (Erol Gelenbe, Ricardo Lent, and Georgia Sakellarii, Eds.), Lecture Notes in Electrical Engineering 62, pp. 277-281, Springer 2011.
    Abstract.
50. Sanjay Jain, Frank Stephan, and Thomas Zeugmann, On the Amount of Nonconstructivity in Learning Formal Languages from Positive Data, in “Theory and Applications of Models of Computation, 9th Annual Conference, TAMC 2012, Beijing, China, May 16-21, 2012, Proceedings,” (Manindra Agrawal and S. Barry Cooper and Ansheng Li, Eds.) Lecture Notes in Computer Science 7287, pp. 423-434, Springer 2012.
    Abstract.
51. Rūsiņš Freivalds, Thomas Zeugmann, and Grant R. Pogosyan, On the Size Complexity of Deterministic Frequency Automata, in “Language and Automata Theory and Applications, 7th International Conference, LATA 2013, Bilbao, Spain, April 2-5, 2013, Proceedings,” (Adrian-Horia Dediu, Carlos Martín-Vide, and Bianca Truthe, Eds.), Lecture Notes in Computer Science 7810, pp. 287-298, Springer 2013.
    Abstract.
52. Rūsiņš Freivalds and Thomas Zeugmann, Active Learning of Recursive Functions by Ultrametric Algorithms, in “SOFSEM 2014: Theory and Practice of Computer Science, 40th International Conference on Current Trends in Theory and Practice of Computer Science, Nový Smokovec, Slovakia, January 26-29, 2014, Proceedings,” (Viliam Geffert, Bart Preneel, Branislav Rovan, Július Štuller, and A Min Tjoa, Eds.), Lecture Notes in Computer Science 8327, pp. 246-257, Springer International Publishing Switzerland 2014.
    Abstract.
53. Yu Zhu and Thomas Zeugmann, Image Analysis in a Parameter-Free Setting, in “Information Sciences and Systems 2015, 30th International Symposium on Computer and Information Sciences (ISCIS 2015),” (Omer H. Abdelrahman, Erol Gelenbe, Gokce Gorbil, and Ricardo Lent, Eds.), Lecture Notes in Electrical Engineering 363, pp. 285-294, Springer International Publishing 2015.
    Abstract.
54. Ziyuan Gao, Sanjay Jain, Frank Stephan, and Thomas Zeugmann, On the Help of Bounded Shot Verifiers, Comparers, and Standardisers in Inductive Inference, in“Proceedings of Algorithmic Learning Theory,” (Firdaus Janoos, Mehryar Mohri, and Karthik Sridharan, Eds.), Proceedings of Machine Learning Research Vol. 83, pp. 413-437, 2018.
    Abstract.
55. Thomas Zeugmann, On the Interplay Between Inductive Inference of Recursive Functions, Complexity Theory and Recursive Numberings, in“Beyond the Horizon of Computability, 16th Conference on Computability in Europe, CiE 2020, Fisciano, Italy, June 29--July 3, 2020, Proceedings,” (Marcella Anselmo, Gianluca Della Vedova, Florin Manea, and Arno Pauly, Eds.), Lecture Notes in Computer Science 12093, pp. 124-136, Springer International Publishing, Cham, Switzerland, 2020.
    Abstract.

"thomas" at "ist.hokudai.ac.jp"

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