Recently Published Papers[1] Thomas Zeugmann, Algorithmisches Lernen von Funktionen und Sprachen, Habilitationschrift, Fachbereich Informatik der TH Darmstadt, November 1993.

1. Books[1] Thomas Zeugmann, Shin-ichi Minato, and Yoshiaki Okubo, Theory ofComputation, Corona Publishing Co., LTD, 2009; ISBN: 978-4-339-02438-8[2] Werner Römisch and Thomas Zeugmann, Mathematical Analysis and theMathematics of Computation, Springer International Publishing AG, October 2016.

2. Journal Papers[1] Thomas Zeugmann, A posteriori Characterizations in Inductive Inference of Recursive Functions. Journal of Information Processing and Cybernetics (EIK) 19,
1983, 559 – 594.
[2] Thomas Zeugmann, On the Synthesis of Fastest Programs in Inductive Inference.
Journal of Information Processing and Cybernetics (EIK) 19, 1983, 625 – 642.
[3] Thomas Zeugmann, On the Nonboundability of Total Effective Operators. Zeitschrift f. Mathematische Logik und Grundlagen der Mathematik (ZML) 30, 1984,
169 – 172.
[4] Efim B. Kinber and Thomas Zeugmann, Inductive Inference of Almost Everywhere Correct Programs by Reliably Working Strategies. Journal of Information
Processing and Cybernetics (EIK) 21, 1985, 91 – 100.
[5] Thomas Zeugmann, On the Power of Recursive Optimizers. Theoretical Computer
Science 62, 1988, 289 – 310.
[6] Thomas Zeugmann, Improved Parallel Computations in the Ring Z/pα . Journalof Information Processing and Cybernetics (EIK) 25, 1989, 543 – 547.
[7] Efim B. Kinber and Thomas Zeugmann, One-Sided Error Probabilistic Inductive Inference and Reliable Frequency Identification. Information & Computation
Vol. 92, No. 2, 1991, 253 – 284.
[8] Thomas Zeugmann, Highly Parallel Computations modulo a Number Having only
Small Prime Factors, Information & Computation Vol. 96, No. 1, 1992, 95 – 114.
[9] Steffen Lange and Thomas Zeugmann, Learning Recursive Languages with
Bounded Mind Changes, International Journal of Foundations of Computer ScienceVol. 4, No. 2, 1993, 157 – 178.

1

[10] Steffen Lange and Thomas Zeugmann, Characterization of Language Learning from Informant under various Monotonicity Constraints, in Journal of Experimental & Theoretical Artificial Intelligence 6, No. 1, 1994, 73 – 94, Special Issue
Algorithmic Learning Theory.
[11] Rolf Wiehagen and Thomas Zeugmann, Ignoring Data May be the Only Way
to Learn Efficiently, in Journal of Experimental & Theoretical Artificial Intelligence
6, No. 1, 1994, 131 – 144, Special Issue Algorithmic Learning Theory.
[12] Steffen Lange and Thomas Zeugmann, Trading Monotonicity Demands versus Efficiency, Bulletin of Informatics and Cybernetics 27, No. 1, 1995, 53 – 83,
Special Issue Kitagawa Symposium, May 1994.
[13] William I. Gasarch, Efim B. Kinber, Mark G. Pleszkoch, Carl H.
Smith, and Thomas Zeugmann, Learning via Queries with Teams and Anomalies, Fundamenta Informaticae 23, No. 1, 1995, 67 – 89.
[14] Thomas Zeugmann, Steffen Lange, and Shyam Kapur, Characterizationsof Monotonic and Dual Monotonic Language Learning, Information & Computation
120, No. 2, 1995, 155 – 173.
[15] Steffen Lange, Thomas Zeugmann, and Shyam Kapur, Monotonic andDual Monotonic Language Learning, Theoretical Computer Science 155, No. 2,
1996, 365 – 410.
[16] Steffen Lange and Thomas Zeugmann, Incremental Learning from Positive
Data, Journal of Computer and System Sciences 53, No. 1, 1996, 88 – 103.
[17] S. Lange and T. Zeugmann, Set-Driven and Rearrangement-Independent Learning of Recursive Languages, Mathematical Systems Theory 29, No. 6, 1996, 599 –
634.
[18] Carl H. Smith, Rolf Wiehagen, and Thomas Zeugmann, Classifying Predicates and Languages, International Journal of Foundations of Computer Science8, No. 1, 1997, 15 – 41.
[19] Thomas Zeugmann, Lange and Wiehagen’s Pattern Language Learning Algorithm: An Average-Case Analysis with respect to its Total Learning Time, Annalsof Mathematics and Artificial Intelligence 23, No. 1-2, 1998, 117 – 145. (Special
Issue for ALT ’94 and AII ’94).
[20] John Case, Sanjay Jain, Steffen Lange, and Thomas Zeugmann, Incremental Concept Learning for Bounded Data Mining, Information & Computation
152, No. 1, 1999, 74 – 110.
[21] Rüdiger Reischuk and Thomas Zeugmann, An Average-Case Optimal OneVariable Pattern Language Learner, Journal of Computer and System Sciences 60,
No. 2, 2000, 302 – 335. (Special Issue for COLT ’98).

2[22] Sanjay Jain, Efim Kinber, Steffen Lange, Rolf Wiehagen, and Thomas
Zeugmann, Learning Languages and Functions by Erasing, Theoretical Computer
Science 241, No. 1/2, 2000, 143 – 189. (Special Issue for ALT ’96).
[23] Peter Rossmanith and Thomas Zeugmann, Stochastic Finite Learning of thePattern Languages, Machine Learning 44, No. 1/2, 2001, 67 – 91. (Special Issue on
Automata Induction, Grammar Inference, and Language Acquisition).
[24] 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, Theoretical Computer
Science 261, Issue 1, 2001, 119 – 156. (Special Issue for ALT ’97).
[25] Frank Stephan and Thomas Zeugmann, Learning Classes of Approximations
to Non-Recursive Functions, Theoretical Computer Science 288, Issue 2, 2002, 309– 341. (Special Issue for ALT ’99).
[26] Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas Zeugmann, On
Learning of Functions Refutably, Theoretical Computer Science 298, Issue 1, 2003,
111 – 143. (Selected Papers in honour of Setsuo Arikawa).
[27] Steffen Lange, Gunter Grieser, and Thomas Zeugmann , Inductive Inference of Approximations for Recursive Concepts Theoretical Computer Science 348,
Issue 1, 2005, 15 – 40. (Special Issue Algorithmic Learning Theory (ALT 2000)).
[28] Thomas Zeugmann, From Learning in the Limit to Stochastic Finite Learning,
Theoretical Computer Science 364, Issue 1, 2006, 77 – 97. (Special Issue for ALT
2003).
[29] John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann, Learning a Subclass of Regular Patterns in Polynomial Time,
Theoretical Computer Science 364, Issue 1, 2006, 115— 131. (Special Issue for ALT
2003).
[30] Thomas Zeugmann and Sandra Zilles, Learning recursive functions: A survey,
Theoretical Computer Science 397, Issue 1-3, 2008, 4 – 56. (Special Issue Forty Yearsof Inductive Inference: Dedicated to the 60th Birthday of Rolf Wiehagen).
[31] Steffen Lange, Thomas Zeugmann, and Sandra Zilles, Learning indexedfamilies of recursive languages from positive data: A survey, Theoretical Computer
Science 397, Issue 1-3, 2008, 194 – 232.
(Special Issue Forty Years of Inductive Inference: Dedicated to the 60th Birthdayof Rolf Wiehagen).
[32] Yohji Akama and Thomas Zeugmann, Consistent and coherent learning with
δ-delay, Information & Computation 206, Issue 11, 2008, 1362 – 1374.
[33] d d d d, d d d, d d d d d d d d d d, A Method of ZBDD Variable Ordering for Frequent Pattern Mining (ddddddddddddddddddddd3

dBDD dddddddddddddd), , The IEICE Transactions on Information and Systems (Japanese Edition) ddddddddddd D”, J91-D, No. 3,
2008, 608 – 618.
[34] Frank J. Balbach and Thomas Zeugmann, Teaching Randomized Learners
with Feedback, Information & Computation 209, Issue 3, 2011, 296 – 319.
(Special Issue for LATA 2009).
[35] Charles Jordan and Thomas Zeugmann, Testable and Untestable Classes ofFirst-Order Formulae, Journal of Computer and System Sciences, 78, Issue 5, 2012,
1557 – 1578.
[36] Ilja Kucevalovs, Ojārs Krasts, Rūsiņš Freivalds, and Thomas Zeugmann, On the Influence of Technology on Learning Processes, Parallel Processing
Letters, 24, Issue 2, 2014, 63 – 79.
[37] Thomas Zeugmann, Obituary Rūsiņš Mārtiņš Freivalds (1942–2016), Bulletin of
the EATCS 118, pp. 17–20, 2016.
[38] Charles Jordan and Thomas Zeugmann, The Kahr–Moore–Wang Class Contains Untestable Properties, Baltic Journal of Modern Computing, 4, Issue 4, 2016,
736 – 752.

3. Book Chapters[1] Thomas Zeugmann, Parallel Algorithms, in “Encyclopedia of Computer Scienceand Technology,” Vol. 21, Supplement 6, (Allen Kent and James G. Williams,
Eds.), pp. 223 – 244, Marcel Dekker Inc. New York and Basel, 1990.
[2] Rolf Wiehagen and Thomas Zeugmann, Learning and Consistency, in “Algorithmic Learning for Knowledge-Based Systems” (Klaus P. Jantke and Steffen
Lange, Eds.), Lecture Notes in Artificial Intelligence 961, pp. 1 – 24, Springer-Verlag
1995.
[3] Rolf Wiehagen, Carl H. Smith, and Thomas Zeugmann, Classifying recursive predicates and languages, in “Algorithmic Learning for Knowledge-Based
Systems” (Klaus P. Jantke and Steffen Lange, Eds.), Lecture Notes in Artificial
Intelligence 961, pp. 177 – 192, Springer-Verlag 1995.
[4] Thomas Zeugmann and Steffen Lange, A Guided Tour Across the Boundaries
of Learning Recursive Languages, in “Algorithmic Learning for Knowledge-Based
Systems” (Klaus P. Jantke and Steffen Lange, Eds.), Lecture Notes in Artificial
Intelligence 961, pp. 193 – 262, Springer-Verlag 1995.
[5] Björn Hoffmeister and Thomas Zeugmann, Text Mining Using Markov
Chains of Variable Length, in “Federation over the Web: International Workshop,
Dagstuhl Castle, Germany, May 1-6, 2005. Revised Selected Papers” (Klaus P. Jantke, Aran Lunzer, Nicolas Spyratos, and Yuzuru Tanaka, Eds.), Lecture Notes in
Computer Science 3847, pp. 1 – 24, Springer-Verlag 2006.
4[6] Yohji Akama and Thomas Zeugmann, Consistency Conditions for Inductive
Inference of Recursive Functions, in “New Frontiers in Artificial Intelligence, JSAI2006 Conference and Workshops, Tokyo, Japan, June 2006, Revised Selected Papers,” (Takashi Washio, Ken Satoh, Hideaki Takeda, and Akihiro Inokuchi, Eds.),
Lecture Notes in Artificial Intelligence 4384, pp. 251 – 264, Springer 2007.
[7] Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann, N-gram Analysis Based on Zero-Suppressed BDDs, in “New Frontiers in Artificial Intelligence,
JSAI 2006 Conference and Workshops, Tokyo, Japan, June 2006, Revised Selected
Papers,” (Takashi Washio, Ken Satoh, Hideaki Takeda, and Akihiro Inokuchi, Eds.),
Lecture Notes in Artificial Intelligence 4384, pp. 289 – 300, Springer 2007.
[8] Kimihito Ito, Thomas Zeugmann, and Yu Zhu, Clustering the Normalized
Compression Distance for Influenza Virus Data, in “Algorithms and Applications,
Essays Dedicated to Esko Ukkonen on the Occasion of His 60th Birthday,” (Tapio
Elomaa, Heikki Mannila, and Pekka Orponen, Eds.), Lecture Notes in Computer
Science 6060, pp. 130 – 146, Springer 2010.
[9] Thomas Zeugmann, PAC Learning, in “Encyclopedia of Machine Learning,”(Claude Sammut and Geoffrey I. Webb, Eds.), pp. 745 – 753, Springer 2010 (Invitedcontribution).
[10] Thomas Zeugmann, Stochastic Finite Learning, in “Encyclopedia of Machine
Learning,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 925 – 928, Springer
2010 (Invited contribution).
[11] Thomas Zeugmann, VC Dimension, in “Encyclopedia of Machine Learning,”(Claude Sammut and Geoffrey I. Webb, Eds.), pp. 1021 – 1024, Springer 2010
(Invited contribution).
[12] Thomas Zeugmann, Epsilon Nets, in “Encyclopedia of Machine Learning,” (Claude
Sammut and Geoffrey I. Webb, Eds.), pp. 326 - 327, Springer 2010 (Invited contribution).
[13] Thomas Zeugmann, Epsilon Covers, in “Encyclopedia of Machine Learning,”(Claude Sammut and Geoffrey I. Webb, Eds.), pp. 326, Springer 2010 (Invitedcontribution).

4. Refereed Conference Papers[1] Thomas Zeugmann, On the Finite Identification of Fastest Programs,in “Symposium on Mathematical Foundations of Computer Science, December 6-11,
1982, Diedrichshagen,” (Helena Rasiowa and Helmut Thiele, Eds.), Seminarbericht
Nr. 52, Sektion Mathematik, Humboldt-Universität zu Berlin”, Berlin, Germany,pp. 151 – 159, 1983.
[2] Thomas Zeugmann, On Recursive Optimizers, in “Proc. International Workshop
on Mathematical Methods of Specification and Synthesis of Software Systems,”1985,
5

(K.P. Jantke and W. Bibel, Eds.), Mathematical Research Vol. 31, pp. 240 – 245,
Akademie Verlag 1985.
[3] Thomas Zeugmann, On Barzdin’s Conjecture, in “Proc. International Workshop
on Analogical and Inductive Inference–AII’ 86,” (K.P. Jantke, Ed.), Lecture Notes
in Computer Science 265, pp. 220 – 227, Springer-Verlag 1987.
[4] Efim Kinber and Thomas Zeugmann, Monte-Carlo Inference and its Relations
to Reliable Frequency Identification. in “Proc. Fundamentals of Computation Theory – FCT’ 1989,” (J. Csirik, J. Demetrovics, F. Gécseg, Eds.), Lecture Notes in
Computer Science 380, pp. 257 – 266, Springer-Verlag 1989.
[5] Efim B. Kinber and Thomas Zeugmann, Refined Query Inference. in “Proc.
International Conference on Analogical and Inductive Inference–AII’89,” (Klaus
P. Jantke, Ed.), Lecture Notes in Artificial Intelligence 397, pp. 148 – 160, SpringerVerlag 1989.
[6] Efim B. Kinber, William I. Gasarch, Thomas Zeugmann, Mark G.
Pleszkoch, and Carl H. Smith, Learning via Queries with Teams and Anomalies, in “Proceedings of the Third Annual Workshop on Computational Learning
Theorie,” Rochester, August 6th - 8th, 1990, (M.A. Fulk, J. Case, Eds.), pp. 327 –
337, Morgan Kaufmann Publishers Inc.
[7] Thomas Zeugmann, Computing Large Polynomial Powers Very Fast in Parallel, in“Proc. Mathematical Foundations of Computer Science,” August 1990, (B. Rovan,Ed.), Lecture Notes in Computer Science 452, pp. 538 – 544, Springer-Verlag 1990.
[8] Thomas Zeugmann, Inductive Inference of Optimal Programs–a Survey and Open
Problems, in in “Proc. 1st International Workshop on Nonmonotonic and Inductive Logic,” December 1990, Karlsruhe, (J. Dix, K.P. Jantke, P.H. Schmitt, Eds.),
Lecture Notes in Artificial Intelligence 543, pp. 208 – 222, Springer-Verlag 1991.
[9] 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.
[10] Steffen Lange and Thomas Zeugmann, A Unifying Approach to Monotonic
Language Learning on Informant, in “Proc. 3rd International Workshop on Analogical and Inductive Inference–AII’92,” Dagstuhl, Oktober 1992, K.P. Jantke, (ed.)
Lecture Notes in Artificial Intelligence 642, pp. 244 – 259, Springer-Verlag 1992.
[11] Rolf Wiehagen and Thomas Zeugmann, Too Much Information Can be too
Much for Efficient Learning, (invited paper), in “Proc. 3rd International Workshop
on Analogical and Inductive Inference–AII’92,” Dagstuhl, Oktober 1992, K.P. Jantke, (ed.) Lecture Notes in Artificial Intelligence 642, pp. 72 – 86, Springer-Verlag
1992.

6

[12] Steffen Lange and Thomas Zeugmann, Monotonic Versus Non–monotonic
Language Learning, in “Proc. 2nd International Workshop on Nonmonotonic andInductive Logic,” Reinhardsbrunn, Dezember 1991, G. Brewka, K.P. Jantke and
P.H. Schmitt (Eds.), Lecture Notes in Artificial Intelligence 659 pp. 254 – 269,
Springer-Verlag 1993.
[13] Steffen Lange and Thomas Zeugmann, Language Learning with a Bounded
Number of Mind Changes, in “Proc. 10th Annual Symposium on Theoretical Aspects of Computer Science,” Würzburg, February 1993, Lecture Notes in Computer
Science 665, pp. 682 – 691, Springer-Verlag 1993.
[14] 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.
[15] Rolf Wiehagen, Carl H. Smith, and Thomas Zeugmann, Classification ofPredicates 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.
[16] Steffen Lange and Thomas Zeugmann, Set-Driven and Rearrangement-Independent Learning of Recursive Languages, in “Proc. 5th International Workshop on
Algorithmic Learning Theory,” Reinhardsbrunn, October 1994, (S. Arikawa andK.P. Jantke, Eds.), Lecture Notes in Artificial Intelligence 872, pp. 453 – 468,
Springer-Verlag 1994.
[17] Steffen Lange and Thomas Zeugmann, Trading Monotonicity Demands versus Mind Changes, in “Proc. Computational Learning Theory, 2nd European Conference, EuroColt’95,” Barcelona, Spain, March 1995, (P. Vitanyi, Ed.), Lecture
Notes in Artificial Intelligence 904, pp. 125 – 139, Springer-Verlag 1995.
[18] 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.
[19] Rusins Freivalds and Thomas Zeugmann, Co–Learning of Recursive Languages from Positive Data, in “Proc. Perspectives of System Informatics,” Second
International Andrei Ershov Memorial Conference, Akademgorodok, Novosibirsk,
Russia, June 1996, (D. Bjørner, M. Broy and I. Pottosin, Eds.), Lecture Notes in
Computer Science 1181, pp. 122 – 133, Springer-Verlag 1996.
[20] Steffen Lange, Rolf Wiehagen, and Thomas Zeugmann, Learning by
Erasing, in “Proc. 7th International Workshop on Algorithmic Learning Theory,”
Sydney, October 1996, (S. Arikawa and A.K. Sharma, Eds.), Lecture Notes in Artificial Intelligence 1160, pp. 228 – 241, Springer-Verlag 1996.

7

[21] John Case, Sanjay Jain, Steffen Lange, and Thomas Zeugmann, Learning Concepts Incrementally for Bounded Data Mining, “Electronic Proc. Automata
Induction, Grammatical Inference, and Language Acquisition,” Workshop at the
14th International Conference on Machine Learning (ICML-97), Nashville, Tennessee, July 12, 1997,
http://www.cs.iastate.edu/ honavar/mlworkshop.html#proceedings[22] 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 “Proc. 8th International Workshop on Algorithmic Learning Theory - ALT’97,” Sendai, Japan, October 1997, (M. Li and A. Maruoka, Eds.), Lecture Notes in Artificial Intelligence
1316, pp. 260 – 276, Springer-Verlag 1997.
[23] Peter Rossmanith and Thomas Zeugmann, Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data, in “Proc. 4thInternational Colloquium on Grammatical Inference - ICGI’98,” Ames, Iowa, July
1998, (V. Honavar and G. Slutzki, Eds.), Lecture Notes in Artificial Intelligence
1433, pp. 13 – 24, Springer-Verlag 1998.
[24] 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.
[25] Rüdiger Reischuk and Thomas Zeugmann, A Complete and Tight AverageCase Analysis of Learning Monomials, in “Proc. 16th International Symposiumon Theoretical Aspects of Computer Science - STACS’99,” Trier, Germany, March
1999, (C. Meinel and S. Tison, Eds.), Lecture Notes in Computer Science 1563, pp.
414 – 423, Springer-Verlag 1999.
[26] Frank Stephan and Thomas Zeugmann, On the Uniform Learnability ofApproximations to Non-Recursive Functions, in “Proc. 10th International Conference on Algorithmic Learning Theory - ALT’99,” Tokyo, Japan, December 1999,(O. Watanabe and T. Yokomori, Eds.), Lecture Notes in Artificial Intelligence 1720,
pp. 276 – 290, Springer-Verlag 1999.
[27] Frank Stephan and Thomas Zeugmann, Average-Case Complexity of Learning Polynomials, in “Proc. 13th Annual Conference on Computational Learning
Theory - COLT’2000,” June 28th – July 1st, Stanford University, Palo Alto, (N.
Cesa-Bianchi and S. Goldman, Eds.) pp. 59 – 68, Morgan Kaufmann Publ. 2000.
[28] Gunter Grieser, Steffen Lange, and Thomas 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-Verlag 2000.

8

[29] 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.
[30] 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.
[31] 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.
[32] 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 andEiji Takimoto, Eds.), Lecture Notes in Artificial Intelligence 2842, pp. 234 – 246,
Springer 2003.
[33] Frank J. Balbach and T. 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 andEtsuji Tomita, Eds.), Lecture Notes in Artificial Intelligence 3734, pp. 474 - 489,
Springer 2005.
[34] Thomas Zeugmann, Inductive Inference and Language Learning, (invited paper),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.
[35] 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.
[36] 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 fürMedien und Kommunikationswissenschaft, Technische Universität Ilmenau No. 21,
pp. 61 - 69, July 2006.
9

[37] 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.
[38] 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 Lavrac̆ and Klaus P. Jantke, Eds.), Lecture Notes in
Artificial Intelligence 4265, pp. 197 - 208, Springer 2006.
[39] Haruya Iwasaki, Shin-ichi Minato, and Thomas Zeugmann, A Method of
Variable Ordering for Zero-suppressed Binary Decision Diagrams in Data Mining
Applications, in “Proc. of The Third IEEE International Workshop on Databases
for Next-Generation Researchers, SWOD 2007,” pp. 85 - 90, 2007, IEEE.
[40] Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann, Unordered Ngram 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.
[41] 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.
[42] 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
2009, Proceedings,” (Adrian Horia Dediu and Armand Mihai Ionescu and Carlos
Martı́n-Vide, Eds.), Lecture Notes in Computer Science 5457, pp. 1 - 18, Springer
2009.
[43] 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.
[44] 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.

10

[45] 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 Martı́n-Vide,
Eds.), Lecture Notes in Computer Science 6031, pp. 333–343, Springer 2010.
[46] 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 Kratochvı́l, Angsheng Li, Jiřı́ Fiala, and Petr Kolman, Eds.), Lecture Notes in
Computer Science 6108, pp. 296–307, Springer 2010.
[47] 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 andHakki Toroslu and Adnan Yazici, Eds.), Lecture Notes in Electrical Engineering 62,
pp. 365-371, Springer 2010.
[48] Charles Jordan and Thomas Zeugmann, Untestable Properties in the KahrMoore-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.
[49] Rūsiņš Freivalds and Thomas Zeugmann, On the Amount of Nonconstructivity in Learning Recursive Functions, in “Theory and Applications of Models ofComputation, 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.
[50] Frank Stephan, Ryo Yoshinaka, and Thomas Zeugmann, On the Parameterised Complexity of Learning Patterns, in “@Computer and Information SciencesII, 26th International Symposium on Computer and Information Sciences,” (Erol
Gelenbe, Ricardo Lent, and Georgia Sakellarii, Eds.), pp. 277-281, Springer 2011.
[51] Sanjay Jain and Frank Stephan and Thomas Zeugmann, On the Amountof 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.
[52] Rūsiņš Freivalds, Thomas Zeugmann, and Grant R. Pogosyan, On theSize 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 Martı́n-Vide,and Bianca Truthe, Eds.), Lecture Notes in Computer Science 7810, pp. 287–298,
Springer 2013.
11

[53] 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 andPractice 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
2014.
[54] 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.
[55] Ziyuan Gao, Sanjay Jain, Frank Stephan, and Thomas Zeugmann, On
the Help of Bounded Shot Verifiers, Comparators, and Standardisers for Learnability
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.

5. Editorial Work
[1] Klaus P. Jantke, Takeshi Shinohara, and Thomas Zeugmann (Eds.), Algorithmic Learning Theory, 6th International Workshop, ALT’95, Fukuoka, Japan,
October 1995, Proceedings, Lecture Notes in Artificial Intelligence 997, SpringerVerlag 1995.
[2] Thomas Zeugmann (Guest Editor), Algorithmic Learning Theory, 6th International Workshop, ALT’95, Fukuoka, Japan, October 1995, Special Issue in Theoretical Computer Science Vol. 185, No. 1, October 10, 1997.
[3] Michael M. Richter, Carl H. Smith, Rolf Wiehagen, and Thomas
Zeugmann (Eds.), Algorithmic Learning Theory, 9th International Conference,
ALT’98, Otzenhausen, Germany, October 1998, Proceedings, Lecture Notes in Artificial Intelligence 1501, Springer-Verlag 1998.
[4] Steffen Lange and Thomas Zeugmann (Eds.), Workshop on Applied Learning
Theory, Proccedings, University of Kaiserslautern, October 7, 1998, DOI Technical
Report DOI-TR 154, Department of Informatics, Kyushu University, October 1998.
[5] Rolf Wiehagen and Thomas Zeugmann, (Guest Editors), Algorithmic Learning Theory, Otzenhausen, Germany, 8 – 10 October 1998, Special Issue in TheoreticalComputer Science Vol. 286, No. 2, October 17, 2001.
[6] Naoki Abe, Roni Khardon, and Thomas Zeugmann (Eds.), Algorithmic
Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA,
November 2001, Proceedings, Lecture Notes in Artificial Intelligence 2225, SpringerVerlag 2001.
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[7] Takeshi Shinohara, Carl H. Smith, and Thomas Zeugmann,(Guest Editors), Selected Papers in Honour of Setsuo Arikawa, Special Issue in TheoreticalComputer Science Vol. 298, Issue 1, April 4, 2003.
[8] N. Cesa-Bianchi, R. Reischuk, and T. Zeugmann (Guest Editors), Algorithmic Learning Theory, Special Issue in Theoretical Computer Science Vol. 350, Issue
1, January 18, 2006.
[9] Shai Ben-David, John Case, and Thomas Zeugmann (Eds.), Algorithmic
Learning Theory, Padova, October 2004, Special Issue in Theoretical Computer Science Vol. 382, Issue 3, September 6, 2007.
[10] John Case, Takeshi Shinohara, Thomas Zeugmann, and Sandra Zilles,(Guest Editors), Forty Years of Inductive Inference: Dedicated to the 60th Birthdayof Rolf Wiehagen, Special Issue in Theoretical Computer Science Vol. 397, Issue 1-3,
May 20, 2008.
[11] Yoav Freund, László Győrfi, Győrgy Turán, and Thomas Zeugmann(Eds.), Algorithmic Learning Theory, 19th International Conference, ALT 2008,
Budapest, Hungary, October 13-16, 2008, Proceedings, Lecture Notes in Artificial
Intelligence 5254, Springer, 2008.
[12] Ricard Gavaldà, Gábor Lugosi, Thomas Zeugmann, and Sandra Zilles(Eds.), Algorithmic Learning Theory, 20th International Conference, ALT 2009,
Porto, Portugal, October 2009, Proceedings, Lecture Notes in Artificial Intelligence5809, Springer, 2009.
[13] Osamu Watanabe and Thomas Zeugmann (Eds.), Stochastic Algorithms: Foundations and Applications, 5th International Symposium, SAGA 2009, Sapporo,
Japan, October 2009, Proceedings, Lecture Notes in Computer Science 5792, Springer, 2009.
[14] László Győrfi, Győrgy Turán, and Thomas Zeugmann, (Guest Editors),
Algorithmic Learning Theory (ALT 2008), Special Issue in Theoretical Computer
Science Vol. 411, Issues 29-30, June 17, 2010.
[15] Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann, (Eds.), Algorithmic Learning Theory, 21st International Conference, ALT
2010, Canberra, Australia, October 6-8, 2010, Proceedings, Lecture Notes in Artificial Intelligence 6331, Springer, 2010.
[16] Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, and Thomas Zeugmann (Eds.), Algorithmic Learning Theory, 22nd International Conference, ALT
2011, Espoo, Finland, October 5-7, 2011, Proceedings, Lecture Notes in Artificial
Intelligence 6925, Springer, 2011.
[17] Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, and Thomas Zeugmann (Eds.), Algorithmic Learning Theory, 23rd International Conference, ALT
2012, Lyon, France, October 29-31, 2012, Proceedings, Lecture Notes in Artificial
Intelligence 7568, Springer, 2012.
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[18] Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann, (Guest Editors), Algorithmic Learning Theory, Special Issue in TheoreticalComputer Science Vol. 473, February 18, 2013.
[19] Sanjay Jain, Rémi Munos, Frank Stephan, and Thomas Zeugmann (Eds.),
Algorithmic Learning Theory, 24t International Conference, ALT 2013, Singapore,
October 6-9, 2013, Proceedings, Lecture Notes in Artificial Intelligence 8139, Springer, 2013.
[20] Jyrki Kivinen, Csaba Szepesvári, and Thomas Zeugmann, (Guest Editors),
Algorithmic Learning Theory, Special Issue in Theoretical Computer Science Vol.
519, January 30, 2014.
[21] Peter Auer, Alexander Clark, Thomas Zeugmann, and Sandra Zilles,(Eds.), Algorithmic Learning Theory, 24t International Conference, ALT 2014, Bled
Slovenia, October 8-10, 2014, Proceedings, Lecture Notes in Artificial Intelligence
8776, Springer, 2014.
[22] Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, and Thomas Zeugmann, (Guest Editors), Algorithmic Learning Theory, Special Issue in TheoreticalComputer Science Vol. 558, November 13, 2014.
[23] Sanjay Jain, Rémi Munos, Frank Stephan, and Thomas Zeugmann,(Guest Editors), Algorithmic Learning Theory, Special Issue in Theoretical Computer Science Vol. 620, March 21, 2016.
[24] Alexander Clark, Peter Auer, and Thomas Zeugmann, (Guest Editors),
Algorithmic Learning Theory, Special Issue in Theoretical Computer Science Vol.
650, October 18, 2016.
[25] Thomas Zeugmann, (Guest Editor), Special Issue on Learning Theory and Complexity in Theoretical Computer Science Vol. 733, July 15, 2018.

6. Reports[1] Steffen Lange and Thomas Zeugmann, On the Power of Monotonic Language
Learning, GOSLER–Report 5/92, FB Mathematik und Informatik, TH Leipzig,
Februar 1992.
[2] Steffen Lange and Thomas Zeugmann, Monotonic Language Learning on
Informant, GOSLER–Report 11/92, FB Mathematik und Informatik, TH Leipzig,
July 1992.
[3] Rolf Wiehagen and Thomas Zeugmann, Inconsistency can be Necessary for
Learning in Polynomial Time, GOSLER–Report 13/92, FB Mathematik und Informatik, TH Leipzig, August 1992.
[4] Steffen Lange, Thomas Zeugmann, and Shyam Kapur, Class Preserving
Monotonic and Dual Monotonic Language Learning, GOSLER–Report 14/92, FB
Mathematik und Informatik, TH Leipzig, September 1992.
14

[5] Steffen Lange and Thomas Zeugmann, Learning Recursive Languages with
Bounded Mind Changes, GOSLER–Report 16/92, FB Mathematik und Informatik,
TH Leipzig, September 1992.
[6] Thomas Zeugmann, Steffen Lange, and Shyam Kapur, Characterizationsof Class Preserving Monotonic and Dual Monotonic Language Learning, Technical
Report IRCS 92 - 24, Institute for Research in Cognitive Science, University ofPennsylvania, Philadelphia, 1992.
[7] Steffen Lange and Thomas Zeugmann, The Learnability of Recursive Languages in Dependence on the Hypothesis Space, GOSLER–Report 20/93, FB Mathematik und Informatik, HTWK Leipzig, Juli 1993.
[8] Rolf Wiehagen, Carl H. Smith, and Thomas Zeugmann, Classifying Recursive Predicates and Languages, GOSLER–Report 21/93, FB Mathematik undInformatik, HTWK Leipzig, Dezember 1993.
[9] Steffen Lange and Thomas 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.
[10] Takashi Tabe and Thomas Zeugmann, Two Variations of Inductive Inference ofLanguages from Positive Data, RIFIS Technical Report RIFIS-TR-CS-105, RIFIS,
Kyushu University 33, March 1995.
[11] Rusins Freivalds and Thomas Zeugmann, Co–Learning of Recursive Languages from Positive Data, RIFIS Technical Report RIFIS-TR-CS-110, RIFIS, Kyushu University 33, April 20, 1995.
[12] Thomas Zeugmann, Lange and Wiehagen’s Pattern Language Learning Algorithm: An Average-Case Analysis with respect to its Total Learning Time, RIFISTechnical Report RIFIS-TR-CS-111, RIFIS, Kyushu University 33, April 20, 1995.
[13] Steffen Lange and Thomas Zeugmann, Modeling Incremental Learning from
Positive Data, RIFIS Technical Report RIFIS-TR-CS-117, RIFIS, Kyushu University 33, August 29, 1995.
[14] Steffen Lange, Rolf Wiehagen, and Thomas Zeugmann, Learning by
Erasing, RIFIS Technical Report RIFIS-TR-CS-122, RIFIS, Kyushu University 33,
February 1996.
[15] Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, and Thomas Zeugmann, Efficient Learning of One-Variable Pattern Languages from Positive Examples, DOI Technical Report DOI-TR-128, Department
of Informatics, Kyushu University, December 12, 1996[16] John Case, Sanjay Jain, Steffen Lange, and Thomas Zeugmann, Incremental Concept Learning for Bounded Data Mining, DOI Technical Report DOITR-136, Department of Informatics, Kyushu University, April 1997.
15

[17] Rüdiger Reischuk and Thomas Zeugmann, Learning One-Variable Pattern
Languages in Linear Average Time, DOI Technical Report DOI-TR-140, Department of Informatics, Kyushu University, September 1997.
[18] Peter Rossmanith and Thomas 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.
[19] Rüdiger Reischuk and Thomas Zeugmann, Analyzing the Average-Case Behavior of Conjunctive Learning Algorithms. Technical Report DOI-TR-153, Department of Informatics, Kyushu University, Fukuoka, Japan, August 1998.
[20] Rüdiger Reischuk and Thomas Zeugmann, An Average-Case Optimal OneVariable Pattern Language Learner, Technical Report A-TR-98-22, Institute for
Theoretical Computer Science, Medical University at Lübeck, September 1998.
[21] Frank Stephan and Thomas Zeugmann, Learning Classes of Approximations
to Non-Recursive Functions, DOI Technical Report DOI-TR-166, Department ofInformatics, Kyushu University, July 1999.
[22] Steffen Lange, Gunter Grieser, and Thomas Zeugmann, Learning Approximations of Recursive Concepts, Schriftenreihe der Institute für Informatik/Mathematik, SIIM-TR-A-01-03, Medizinische Universität zu Lübeck, March 4, 2001.
[23] Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas 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.
[24] John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann, A Polynomial Time Learner for a Subclass of Regular Patterns,
Electronic Colloquium on Computational Complexity, Report TR04-038, April 28,
2004.
[25] Thomas Zeugmann, Course Notes on Theory and Practice of Algorithms - Part
I: Algorithmic Learning, TCS Technical Report, Series B, TCS-TR-B-05-1, June 7,2005.
[26] Frank J. Balbach and Thomas Zeugmann, Teaching Learners that can only
Perform Restricted Mind Changes, TCS Technical Report, Series A, TCS-TR-A-055, Division of Computer Science, Hokkaido University, July 18, 2005.
[27] 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.
[28] 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.
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[29] Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann, N-gram Analysis Based on Zero-suppressed BDDs, TCS Technical Report, Series A, TCS-TR-A06-16, Division of Computer Science, Hokkaido University, June 17, 2006.
[30] d d d d, d d d, dddd dddddd , dddddddddddddddd
dddddddddddddddddddd, ddddddd63dddddd
dddddd, SIG-FPAI-A601, pp.65-70, 2006.
[31] 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.
[32] 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.
[33] Jan Poland and Thomas Zeugmann, Clustering based on Graph Cuts, dd
ddddd63dddddddddddd, SIG-FPAI-A601, pp.77-82, 2006.
[34] 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.
[35] 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.
[36] 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, October30, 2007.
[37] 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.
[38] Shane Legg, Jan Poland, and Thomas Zeugmann, On the Limits of Learningwith Computational Models, TCS Technical Report, Series A, TCS-TR-A-08-34,
Division of Computer Science, Hokkaido University, January 16, 2008.
[39] 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.
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[40] 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.
[41] 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 17, 2010.
[42] 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 12, 2012.
[43] Rūsiņš Freivalds and Thomas Zeugmann, Active Learning of Classes of Recursive Functions by Ultrametric Algorithms, TCS Technical Report, Series A, TCSTR-A-13-68, Division of Computer Science, Hokkaido University, October 24, 2013.
[44] Ziyuan Gao, Sanjay Jain, Frank Stephan, and Thomas Zeugmann, On
the Help of Bounded Shot Verifiers, Comparers, and Standardisers in Inductive
Inference, TCS Technical Report, Series A, TCS-TR-A-17-82, Division of Computer
Science, Hokkaido University, November 4, 2017.

18