Hokkaido University
Graduate School of Information Science and Technology
Division of Computer Science
Laboratory for Algorithmics
Thomas Zeugmann

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Hokkaido University

Technical Reports

  1. T. Zeugmann, Inductive Inference of Fast Programs, in Proc. ``Workshop on Algorithms and Computing Theory,'' Sept. 7-10, 1981, M. Karpinski and Z. Habasinski (Eds.), Technical University of Poznan, Poznan 1981, 56 - 64.
  2. T. Zeugmann, On the Finite Identification of Fastest Programs, in Proc. ``Symposium on Mathematical Foundations of Computer Science,'' December 6 - 11, 1982, (H. Rasiowa and H. Thiele, Eds.), Seminarbericht Nr. 52 der Humboldt--Universität zu Berlin, Sektion Mathematik, 1983.
  3. A. Albrecht, H. Jung, and T. Zeugmann, Bericht zum Adreßprozessorentwurf, Technischer Bericht, Humboldt-Universität zu Berlin, 1984.
  4. S. Lange and T. Zeugmann, On the Power of Monotonic Language Learning, GOSLER-Report 5/92, FB Mathematik und Informatik, TH Leipzig, February 1992.
  5. R. Wiehagen and T. Zeugmann, Inconsistency can be Necessary for Learning in Polynomial Time, GOSLER-Report 13/92, FB Mathematik und Informatik, TH Leipzig, August 1992.
  6. S. Lange, T. Zeugmann and S. Kapur, Class Preserving Monotonic and Dual Monotonic Language Learning, GOSLER-Report 14/92, FB Mathematik und Informatik, TH Leipzig, August 1992.
  7. S. Lange and T. Zeugmann, Learning Recursive Languages with Bounded Mind Changes, GOSLER-Report 16/92, FB Mathematik und Informatik, TH Leipzig, September 1992.
  8. T. Zeugmann, S. Lange and S. Kapur, Characterizations of Class Preserving Monotonic and Dual Monotonic Language Learning, Technical Report IRCS 92 - 24, Institute for Research in Cognitive Science, University of Pennsylvania, Philadelphia, 1992.
  9. S. Lange and T. Zeugmann, The Learnability of Recursive Languages in Dependence on the Hypothesis Space, GOSLER-Report 20/93, FB Mathematik und Informatik, HTWK Leipzig, July 1993.
  10. R. Wiehagen, C.H. Smith and T. Zeugmann, Classifying Recursive Predicates and Languages, GOSLER-Report 21/93, FB Mathematik und Informatik, HTWK Leipzig, December 1993.
  11. S. Lange and T. Zeugmann, On the Impact of Order Independence to the Learnability of Recursive Languages, Research Report ISIS-RR-93-17E, FUJITSU Laboratories Ltd., Numazu, November 12, 1993.
  12. T. Zeugmann, Report on COLT 1994, ACM SIGACT News, 25, No. 4, pp. 88-95, 1994.
  13. T. Zeugmann, Report on COLT 1994, ACM SIGART Bulletin, 5, No. 4, pp. 25-27, 1994.
  14. T. Tabe and T. Zeugmann, Two Variations of Inductive Inference of Languages from Positive Data, RIFIS Technical Report RIFIS-TR-CS-105, RIFIS, Kyushu University 33, March 1995.
  15. R. Freivalds and T. Zeugmann, Co-Learning of Recursive Languages from Positive Data, RIFIS Technical Report RIFIS-TR-CS-110, RIFIS, Kyushu University 33, April 20, 1995.
  16. T. Zeugmann, Lange and Wiehagen's Pattern Language Learning Algorithm: An Average-Case Analysis with respect to its Total Learning Time, RIFIS Technical Report RIFIS-TR-CS-111, RIFIS, Kyushu University 33, April 20, 1995. Abstract.
  17. S. Lange and T. Zeugmann, Modeling Incremental Learning from Positive Data, RIFIS Technical Report RIFIS-TR-CS-117, RIFIS, Kyushu University 33, August 29, 1995.
  18. S. Lange, R. Wiehagen and T. Zeugmann, Learning by Erasing, RIFIS Technical Report RIFIS-TR-CS-122, RIFIS, Kyushu University 33, February 13, 1996. Abstract.
  19. T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger, and T. Zeugmann, Efficient Learning of One-Variable Pattern Languages from Positive Data, DOI Technical Report DOI-TR-128, Department of Informatics, Kyushu University, December 12, 1996. Abstract (and link to our pattern language learning page) or as xbm-file Abstract (and same link).
  20. J. Case, S. Jain, S. Lange and T. Zeugmann, Incremental Concept Learning for Bounded Data Mining, DOI Technical Report DOI-TR-136, Department of Informatics, Kyushu University, April 1997. Abstract.
  21. R. Reischuk and T. Zeugmann, Learning One-Variable Pattern Languages in Linear Average Time, DOI Technical Report DOI-TR-140, Department of Informatics, Kyushu University, September 1997. Abstract.
  22. P. Rossmanith and T. Zeugmann, Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data, DOI Technical Report DOI-TR-145, Department of Informatics, Kyushu University, January 1998. Abstract.
  23. R. Reischuk and T. Zeugmann, Analyzing the Average-Case Behavior of Conjunctive Learning Algorithms, DOI Technical Report DOI-TR-153, Department of Informatics, Kyushu University, August 1998. Abstract.
  24. R. Reischuk and T. Zeugmann, An Average-Case Optimal One-Variable Pattern Language Learner, Electronic Colloquium on Computational Complexity, Report TR98-069, December 08, 1998. Abstract.
  25. F. Stephan and T. Zeugmann, Learning Classes of Approximations to Non-Recursive Functions, DOI Technical Report DOI-TR-166, Department of Informatics, Kyushu University, July 1999. Abstract.
  26. S. Lange, G. Grieser and T. Zeugmann, Learning Approximations of Recursive Concepts, Schriftenreihe der Institute für Informatik/Mathematik, SIIM-TR-A-01-03, Medizinische Universität zu Lübeck, March 4, 2001. Abstract.
  27. S. Jain, E. Kinber, R. Wiehagen and T. Zeugmann, Refutable Inductive Inference of Recursive Functions, Schriftenreihe der Institute für Informatik/Mathematik, SIIM-TR-A-01-06, Medizinische Universität zu Lübeck, May 11, 2001. Abstract.
  28. J. Case, S. Jain, R. Reischuk, F. Stephan, and T. Zeugmann. A Polynomial Time Learner for a Subclass of Regular Patterns, Electronic Colloquium on Computational Complexity, Report TR04-038, April 28, 2004. Abstract.
  29. Frank J. Balbach and Thomas Zeugmann. Teaching Learners that can only Perform Restricted Mind Changes, TCS Technical Report, Series A, TCS-TR-A-05-5, Division of Computer Science, Hokkaido University, July 18, 2005. Abstract.
  30. Thomas Zeugmann. From Learning in the Limit to Stochastic Finite Learning, TCS Technical Report, Series A, TCS-TR-A-05-8, Division of Computer Science, Hokkaido University, August 27, 2005. Abstract.
  31. Frank J. Balbach and Thomas Zeugmann. On the Teachability of Randomized Learners, TCS Technical Report, Series A, TCS-TR-A-06-13, Division of Computer Science, Hokkaido University, April 26, 2006. Abstract.
  32. Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann. N-gram Analysis Based on Zero-suppressed BDDs, TCS Technical Report, Series A, TCS-TR-A-06-16, Division of Computer Science, Hokkaido University, June 17, 2006. Abstract.
  33. Jan Poland and Thomas Zeugmann. Fundamental Research for Knowledge Federation In Meme-Media Technology Approach to the R&D of Next-Generation Information Technologies. 21COE Program in Information, Electrics, and Electronics at Hokkaido University. 3rd International Symposium on Ubiquitous Knowledge Network Environment, February 27-March 1, 2006, Sapporo Convention Center, Sapporo, Japan, Presentations of COE Program Members, pp. 23-32, 2006.
  34. Jan Poland and Thomas Zeugmann. Spectral Clustering of the Google Distance, In Meme-Media Technology Approach to the R&D of Next-Generation Information Technologies. 21COE Program in Information, Electrics, and Electronics at Hokkaido University. 3rd International Symposium on Ubiquitous Knowledge Network Environment, February 27-March 1, 2006, Sapporo Convention Center, Sapporo, Japan, Postdoctoral Fellows Presentation, pp. 25-32, 2006.
  35. 岩崎 玄弥, 湊 真一, ツォイクマン トーマス, データベース解析のためのゼロサプレス型二分決定グラフの簡単化に関する考察, 人工知能学会 第63回 人工知能基本問題研究会, SIG-FPAI-A601, pp.65-70, 2006.
  36. Jan Poland and Thomas Zeugmann. Clustering based on Graph Cuts, 人工知能学会 第63回 人工知能基本問題研究会, SIG-FPAI-A601, pp.77-82, 2006.
  37. Thomas Zeugmann. Course Notes on Theory of Computation, TCS Technical Report, Series B, TCS-TR-B-07-2, Division of Computer Science, Hokkaido University, August 15, 2007. Abstract.
  38. Yohji Akama and Thomas Zeugmann. Consistent and Coherent Learning with δ-delay, TCS Technical Report, Series A, TCS-TR-A-07-29, Division of Computer Science, Hokkaido University, October 5, 2007. Abstract.
  39. Steffen Lange, Thomas Zeugmann and Sandra Zilles. Learning Indexed Families of Recursive Languages from Positive Data, TCS Technical Report, Series A, TCS-TR-A-07-31, Division of Computer Science, Hokkaido University, October 30, 2007. Abstract.
  40. Thomas Zeugmann and Sandra Zilles. Learning Recursive Functions, TCS Technical Report, Series A, TCS-TR-A-07-32, Division of Computer Science, Hokkaido University, November 18, 2007. Abstract.
  41. Shane Legg, Jan Poland, and Thomas Zeugmann. On the Limits of Learning with Computational Models, TCS Technical Report, Series A, TCS-TR-A-08-34, Division of Computer Science, Hokkaido University, January 16, 2008. Abstract.
  42. Thomas Zeugmann. Course Notes on Complexity and Cryptography, TCS Technical Report, Series B, TCS-TR-B-08-4, Division of Computer Science, Hokkaido University, April 16, 2008. Abstract.
  43. Charles Jordan and Thomas Zeugmann. Contributions to the Classification for Testability: Four Universal and One Existential Quantifier, TCS Technical Report, Series A, TCS-TR-A-09-39, Division of Computer Science, Hokkaido University, November 2009. Abstract.
  44. Rūsiņš Freivalds and Thomas Zeugmann. On the Amount of Nonconstructivity in the Inductive Inference of Recursive Functions, TCS Technical Report, Series A, TCS-TR-A-10-49, Division of Computer Science, Hokkaido University, December 2010. Abstract.
  45. Sanjay Jain, Frank Stephan, and Thomas Zeugmann.
    On the Amount of Nonconstructivity in Learning Formal Languages from Text,
    TCS Technical Report, Series A, TCS-TR-A-12-55, Division of Computer Science, Hokkaido University, March 2012. Abstract.
  46. Rūsiņš Freivalds and Thomas Zeugmann. Active Learning of Classes of Recursive Functions by Ultrametric Algorithms, TCS Technical Report, Series A, TCS-TR-A-13-68, Division of Computer Science, Hokkaido University, October 2013. Abstract.


For further information, please contact
Thomas Zeugmann
Mailbox "thomas" at "ist.hokudai.ac.jp"


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