1. Thomas Zeugmann,
   A posteriori Characterizations in Inductive Inference of Recursive Functions,
   Journal of Information Processing and Cybernetics (EIK) 19, 1983, 559 - 594.
   Abstract
   
2. Thomas Zeugmann,
   On the Power of Recursive Optimizers,
   Theoretical Computer Science 62, 1988, 289 - 310.
   Abstract
   
3. Efim Kinber and Thomas Zeugmann,
   One-Sided Error Probabilistic Inductive Inference and Reliable Frequency Identification,
   Information & Computation Vol. 92, No. 2, 1991, 253 - 284.
   Abstract.
   
4. 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
   
5. Steffen Lange and Thomas Zeugmann,
   Learning Recursive Languages With a Bounded Number of Mind Changes,
   International Journal of Foundations of Computer Science Vol. 4, No. 2, 1993, 157 - 178.
   Abstract.
   
6. Thomas Zeugmann,
   Algorithmisches Lernen von Funktionen und Sprachen,
   Habilitationschrift, Fachbereich Informatik der TH Darmstadt, November 1993.
   
7. Steffen Lange and Thomas Zeugmann,
   Characterization of Language Learning from Informant under various Monotonicity Constraints ,
   Journal of Experimental & Theoretical Artificial Intelligence 6, No. 1, 1994, 73 - 94.
   Special issue Algorithmic Learning Theory.
   Abstract
   
8. Rolf Wiehagen and Thomas Zeugmann,
   Ignoring Data May be the Only Way to Learn Efficiently,
   Journal of Experimental & Theoretical Artificial Intelligence 6, No. 1, 1994, 131 - 144.
   Special issue Algorithmic Learning Theory.
   Abstract
   
9. Steffen Lange and Thomas Zeugmann,
   Trading Monotonicity Demands versus Efficiency,
   Bulletin of Informatics and Cybernetics 27, No.1, 1995, pp. 53 - 83.
   Abstract
   
10. William I. Gasarch, Efim B. Kinber, Mark G. Pleszkoch, Carl H. Smith, and Thomas Zeugmann,
    Learning via Queries with Teams and Anomalies,
    Fundamenta Informaticae, Vol. 23, Number 1, May 1995, 67-89.
    Abstract.
    
11. Thomas Zeugmann, Steffen Lange, and Shyam Kapur,
    Characterizations of Monotonic and Dual Monotonic Language Learning,
    Information & Computation 120, No. 2, 1995, 155 - 173.
    Abstract
    
12. Rolf Wiehagen and Thomas Zeugmann,
    Learning and Consistency,
    in“Algorithmic Learning for Knowledge-Based Systems,” (K.P. Jantke and S. Lange, Eds.), Lecture Notes in Artificial Intelligence 961, pp. 1 - 24, Springer-Verlag 1995.
    Abstract
    
13. Thomas Zeugmann and Steffen Lange,
    A Guided Tour Across the Boundaries of Learning Recursive Languages,
    in “Algorithmic Learning for Knowledge-Based Systems,” (K.P. Jantke and S. Lange, Eds.), Lecture Notes in Artificial Intelligence 961, pp. 190 - 258, Springer-Verlag 1995.
    Abstract
    
14. Steffen Lange, Thomas Zeugmann, and Shyam Kapur,
    Monotonic and Dual Monotonic Language Learning,
    Theoretical Computer Science 155, No. 2, 1996, 365 - 410.
    Abstract
    
15. Steffen Lange and Thomas Zeugmann,
    Incremental Learning from Positive Data,
    Journal of Computer and System Sciences 53, No. 1, 1996, 88 - 103.
    Abstract
    
16. S. Lange and T. Zeugmann,
    Set-Driven and Rearrangement-Independent Learning of Recursive Languages,
    Mathematical Systems Theory, 29, No. 6, 1996, 599 - 634.
    Abstract
    
17. Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, and Thomas 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-Verlag 1997.
    Abstract
    
18. Peter Rossmanith and Thomas 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-Verlag 1998.
    Abstract.
    
19. Thomas Zeugmann,
    Lange and Wiehagen's Pattern Language Learning Algorithm: An Average-Case Analysis with respect to its Total Learning Time,
    Annals of Mathematics and Artificial Intelligence Vol. 23, No. 1-2, 1998, 117-145.
    (Special Issue for ALT'94 and AII'94).
    Abstract
    or as xbm-file Abstract.
    
20. Rüdiger Reischuk and Thomas 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.
    
21. 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-Verlag 1999.
    Abstract.
    
22. John Case, Sanjay Jain, Steffen Lange and Thomas Zeugmann,
    Incremental Concept Learning for Bounded Data Mining,
    Information & Computation Vol. 152, No. 1, 1999, 74-110.
    Abstract.
    
23. Rüdiger Reischuk and Thomas Zeugmann,
    An Average-Case Optimal One-Variable Pattern Language Learner,
    Journal of Computer and System Sciences Vol. 60, No. 2, 2000, 302-335.
    (Special Issue for COLT'98).
    Abstract.
    
24. Sanjay Jain, Efim Kinber, Steffen Lange, Rolf Wiehagen, and Thomas Zeugmann,
    Learning languages and functions by erasing,
    Theoretical Computer Science Vol. 241, No. 1-2, 2000, 143-189.
    (Special Issue ALT'96).
    Abstract.
    
25. Peter Rossmanith and Thomas Zeugmann,
    Stochastic Finite Learning of the Pattern Languages,
    Machine Learning Vol. 44, No. 1/2, 2001, 67-91.
    (Special Issue on Automata Induction, Grammar Inference, and Language Acquisition),
    Abstract
    
26. Frank Stephan and Thomas Zeugmann,
    Learning Classes of Approximations to Non-Recursive Functions,
    Theoretical Computer Science Vol. 288, Issue 2, 2002, 309-341.
    (Special Issue ALT '99).
    Abstract.
    
27. Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas Zeugmann
    On Learning of Functions Refutably
    Theoretical Computer Science Vol. 298, Issue 1, 2003, 111-143.
    Abstract.
    
28. Steffen Lange, Gunter Grieser, and Thomas Zeugmann
    Inductive Inference of Approximations for Recursive Concepts
    Theoretical Computer Science Vol. 348, Issue 1, 2005, 15-40.
    (Special Issue Algorithmic Learning Theory (ALT 2000))
    Abstract.
    
29. Thomas Zeugmann,
    From Learning in the Limit to Stochastic Finite Learning
    Theoretical Computer Science, Vol. 364, Issue 1, 2006, 77-97.
    (Special Issue Algorithmic Learning Theory (ALT 2003))
    Abstract.
    
30. John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann,
    Learning a Subclass of Regular Patterns in Polynomial Time
    Theoretical Computer Science, Vol. 364, Issue 1, 2006, 115-131.
    (Special Issue Algorithmic Learning Theory (ALT 2003))
    Abstract.
    
31. Thomas Zeugmann and Sandra Zilles,
    Learning recursive functions: A survey
    Theoretical Computer Science, Vol. 397, Issues 1-3, 2008, 4-56.
    (Special Issue Forty Years of Inductive Inference: Dedicated to the 60th Birthday of Rolf Wiehagen)
    Abstract.
    
32. Steffen Lange, Thomas Zeugmann, and Sandra Zilles,
    Learning indexed families of recursive languages from positive data: A survey
    Theoretical Computer Science, Vol. 397, Issues 1-3, 2008, 194-232 .
    (Special Issue Forty Years of Inductive Inference: Dedicated to the 60th Birthday of Rolf Wiehagen)
    Abstract.
33. Yohji Akama and Thomas Zeugmann,
    Consistent and coherent learning with δ-delay,
    Information & Computation, Vol. 206, Issue 11, 2008, 1362-1374.
    Abstract.
34. Frank J. Balbach and Thomas Zeugmann,
    Teaching Randomized Learners with Feedback,
    Information & Computation, Vol. 209, Issue 3, 2011, 296-319.
    (Special Issue for LATA 2009),
    Abstract.
35. 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.

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

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