Publications2011
Journals
Frank J. Balbach and Thomas Zeugmann. Teaching randomized learners with feedback. Information and Computation, 209 no. 3 pp. 296-319, 2011. (LATA 2009), Special Issue.
Shin-ichi Minato. Overview of ERATO Minato Project: The art of discrete structure manipulation between science and engineering. New Generation Computing, 29 no. 2 pp. 223-238, 2011. Invited Paper.
Book Chapters
Shin-ichi Minato and Nicolas Spyratos. BDD-based combinatorial keyword query processing under a taxonomy model. In Gunther Kreuzberger, Aran Lunzer, and Roland Kaschek, editors, Interdisciplinary Advances in Adaptive and Intelligent Assistant Systems: Concepts, Techniques, Applications, and Use, pages 26-39. IGI Global, Hershey, Pennsylvania, 2011.
Editorial Work
Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, and Thomas Zeugmann, editors. Algorithmic Learning Theory, 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings, volume 6925 of Lecture Notes in Artificial Intelligence, Berlin, Heidelberg, New York, October 2011. Springer.
Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, and Thomas Zeugmann. Editors' introduction. In Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, and Thomas Zeugmann, editors, Algorithmic Learning Theory, 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings, volume 6925 of Lecture Notes in Artificial Intelligence, pages 1-13, Berlin, Heidelberg, New York, 2011. Springer.
International Conferences
Shuhei Denzumi, Ryo Yoshinaka, Hiroki Arimura, and Shin-ichi Minato. Notes on sequence binary decision diagrams: Relationship to acyclic automata and complexities of binary set operations. In Jan Holub and Jan Žďárek, editors, Proceedings of the Prague Stringology Conference 2011, pages 147-161, Czech Technical University in Prague, Czech Republic, 2011.
Shuhei Denzumi, Hiroki Arimura, and Shin-ichi Minato. Implementation of sequence BDDs in Erlang. In Erlang '11, Proceedings of the 10th ACM SIGPLAN Workshop on Erlang, pages 90-91, 2011.
Charles Jordan and Thomas Zeugmann. Untestable properties in the Kahr-Moore-Wang class. In Lev D. Beklemishev and Ruy de Queiroz, editors, Logic, Language, Information and Computation, 18th International Workshop, WoLLIC 2011, Philadelphia, PA, USA, May 18-21, 2011, Proceedings, volume 6642 of Lecture Notes in Artificial Intelligence, pages 176-186. Springer, 2011.
Rūsiņš Freivalds and Thomas Zeugmann. On the amount of nonconstructivity in learning recursive functions. In Mitsunori Ogihara and Jun Tarui, editors, Theory and Applications of Models of Computation, 8th Annual Conference, TAMC 2011, Tokyo, Japan, May 23-25, 2011, Proceedings, volume 6648 of Lecture Notes in Computer Science, pages 332-343. Springer, 2011.
Shin-ichi Minato. πDD: A new decision diagram for efficient problem solving in permutation space. In Karem A. Sakallah and Laurent Simon, editors, Theory and Applications of Satisfiability Testing - SAT 2011, 14th International Conference, SAT 2011, Ann Arbor, MI, USA, June 19-22, 2011, Proceedings, volume 6695 of Lecture Notes in Computer Science, pages 90-104. Springer, 2011.
Frank Stephan, Ryo Yoshinaka, and Thomas Zeugmann. On the parameterised complexity of learning patterns. In Erol Gelenbe, Ricardo Lent, and Georgia Sakellari, editors, Computer and Information Sciences II, 26th International Symposium on Computer and Information Sciences, pages 277-281. Springer, 2011.
Technical Reports
Shin-ichi Minato. πDD: A new decision diagram for manipulating sets of permutations. Technical Report TCS-TR-A-11-50, Division of Computer Science, Hokkaido University, 2011.
Ryo Yoshinaka, Jun Kawahara, Shuhei Denzumi, Hiroki Arimura, and Shin-ichi Minato. Counter examples to the conjecture on the complexity of BDD binary operations. Technical Report TCS-TR-A-11-52, Division of Computer Science, Hokkaido University, 2011.
Shuhei Denzumi, Ryo Yoshinaka, Shin-ichi Minato, and Hiroki Arimura. Efficient algorithms on sequence binary decision diagrams for manipulating sets of strings. Technical Report TCS-TR-A-11-53, Division of Computer Science, Hokkaido University, 2011.
2010
Book Chapters
Kimihito Ito, Thomas Zeugmann, and Yu Zhu. Clustering the normalized compression distance for influenza virus data. In Tapio Elomaa, Heikki Mannila, and Pekka Orponen, editors, Algorithms and Applications, Essays Dedicated to Esko Ukkonen on the Occasion of His 60th Birthday, volume 6060 of Lecture Notes in Computer Science, pages 130-146. Springer, Heidelberg, 2010.
Thomas Zeugmann. PAC learning. In Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Learning, pages 745-753. Springer, New York, 2010. Invited contribution.
Thomas Zeugmann. Stochastic finite learning. In Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Learning, pages 925-928. Springer, New York, 2010. Invited contribution.
Thomas Zeugmann. VC dimension. In Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Learning, pages 1021-1024. Springer, New York, 2010. Invited contribution.
Thomas Zeugmann. Epsilon nets. In Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Learning, pages 326-327. Springer, New York, 2010. Invited contribution.
Thomas Zeugmann. Epsilon covers. In Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Learning, page 326. Springer, New York, 2010. Invited contribution.
Editorial Work
László Győrfi, Győrgy Turán, and Thomas Zeugmann. Guest editors' foreword. Theoret. Comput. Sci., 411 no. 29-30 pp. 2629-2631, 2010. Algorithmic Learning Theory (ALT 2008), Special Issue.
Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann, editors. Algorithmic Learning Theory, 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010, Proceedings, volume 6331 of Lecture Notes in Artificial Intelligence, Berlin, Heidelberg, New York, October 2010. Springer.
Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann. Editors' introduction. In Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann, editors, Algorithmic Learning Theory, 21th International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010, Proceedings, volume 6331 of Lecture Notes in Artificial Intelligence, pages 1-10, Berlin, Heidelberg, New York, 2010. Springer.
International Conferences
Masakazu Ishihata, Yoshitaka Kameya, Taisuke Sato, and Shin-ichi Minato. An EM algorithm on BDDs with order encoding for logic-based probabilistic models. In Masashi Sugiyama and Qiang Yang, editors, 2nd Asian Conference on Machine Learning (ACML2010), Tokyo, Japan, Nov. 8-10, 2010, volume 13 of JMLR Workshop and Conference Proceedings, pages 161-176. JMLR: Workshop and Conference Proceedings, 2010.
Kimihito Ito, Thomas Zeugmann, and Yu Zhu. Recent experiences in parameter-free data mining. In Erol Gelenbe, Ricardo Lent, Georgia Sakellari, Ahmet Sacan, Hakki Toroslu, and Adnan Yazici, editors, Computer and Information Science, Proceedings of the 25th International Symposium on Computer and Information Sciences, volume 62 of Lecture Notes in Electrical Engineering, pages 365-371, Heidelberg, 2010. Springer. Invited Paper.
Charles Jordan and Thomas Zeugmann. Untestable properties expressible with four first-order quantifiers. In Adrian-Horia Dediu, Henning Fernau, and Carlos Martín-Vide, editors, Language and Automata Theory and Applications, 4th International Conference, LATA 2010, Trier, Germany, May 2010, Proceedings, volume 6031 of Lecture Notes in Computer Science, pages 333-343. Springer, 2010.
Charles Jordan and Thomas Zeugmann. A note on the testability of Ramsey's class. In Jan Kratochvíl, Angsheng Li, Jiří Fiala, and Petr Kolman, editors, Theory and Applications of Models of Computation, 7th Annual Conference, TAMC 2010, Prague, Czech Republic, June 7-11, 2010, Proceedings, volume 6108 of Lecture Notes in Computer Science, pages 296-307. Springer, 2010.
Yusaku Kaneta, Shin-ichi Minato, and Hiroki Arimura. Fast bit-parallel matching for network and regular expressions. In Edgar Chávez and Stefano Lonardi, editors, String Processing and Information Retrieval - 17th International Symposium, SPIRE 2010, Los Cabos, Mexico, October 11-13, 2010. Proceedings, volume 6393 of Lecture Notes in Computer Science, pages 372-384. Springer, 2010.
Shin-ichi Minato. Discrete structure manipulation for discovery science problems. In Erol Gelenbe, Ricardo Lent, Georgia Sakellari, Ahmet Sacan, Hakki Toroslu, and Adnan Yazici, editors, Computer and Information Science, Proceedings of the 25th International Symposium on Computer and Information Sciences, volume 62 of Lecture Notes in Electrical Engineering, pages 359-364, Heidelberg, 2010. Springer. Invited Paper.
Shin-ichi Minato and Takeaki Uno. Frequentness-transition queries for distinctive pattern mining from time-segmented databases. In Srinivasan Parthasarathy, Bing Liu, and Chandrika Kamath, editors, Proceedings of the Tenth SIAM International Conference on Data Mining, pages 339-349. SIAM, 2010.
Ryo Yoshinaka, Yuichi Kaji, and Hiroyuki Seki. Chomsky-Schützenberger-type characterization of multiple context-free languages. In Adrian-Horia Dediu, Henning Fernau, and Carlos Martín-Vide, editors, Language and Automata Theory and Applications, 4th International Conference, LATA 2010, Trier, Germany, May 2010, Proceedings, volume 6031 of Lecture Notes in Computer Science, pages 596-607. Springer, 2010.
Technical Reports
Shuhei Denzumi, Hiroki Arimura, and Shin-ichi Minato. Substring indices based on sequence BDDs. Technical Report TCS-TR-A-10-42, Division of Computer Science, Hokkaido University, 2010.
Rūsiņš Freivalds and Thomas Zeugmann. On the amount of nonconstructivity in the inductive inference of recursive functions. Technical Report TCS-TR-A-10-49, Division of Computer Science, Hokkaido University, 2010.
Yusaku Kaneta, Shin-ichi Minato, and Hiroki Arimura. An efficient matching algorithm for acyclic regular expressions with bounded depth. Technical Report TCS-TR-A-10-40, Division of Computer Science, Hokkaido University, 2010.
Yusaku Kaneta, Shingo Yoshizawa, Shin-ichi Minato, Hiroki Arimura, and Yoshikazu Miyanaga. Dynamic reconfigurable bit-parallel architecture for large-scale regular expression matching. Technical Report TCS-TR-A-10-45, Division of Computer Science, Hokkaido University, 2010.
Yusaku Kaneta, Shin-ichi Minato, and Hiroki Arimuta. Fast bit-parallel matching for network and regular expressions. Technical Report TCS-TR-A-10-47, Division of Computer Science, Hokkaido University, 2010.
Shin-ichi Minato. Recent and future work on decision diagrams and discrete structure manipulation. Technical Report TCS-TR-B-10-7, Division of Computer Science, Hokkaido University, 2010.
2009
Books
Thomas Zeugmann, Shin-ichi Minato, and Yoshiaki Okubo. Theory of Computation. Corona Publishing Co., LTD, 2009.
Journals
Ryo Yoshinaka. Learning efficiency of very simple grammars from positive data. Theoret. Comput. Sci., 410 no. 19 pp. 1807-1825, 2009. Special issue for ALT 2007.
Ryo Yoshinaka. An elementary proof of a generalization of double Greibach normal form. Information Processing Letters, 109 no. 10 pp. 490-492, 2009.
Editorial Work
Sanjay Chawla, Takashi Washio, Shin-ichi Minato, Shusaku Tsumoto, Takashi Onoda, Seiji Yamada, and Akihiro Inokuchi, editors. New Frontiers in Applied Data Mining, PAKDD 2008 International Workshops, Osaka, Japan, May 20-23, 2008. Revised Selected Papers, volume 5433 of Lecture Notes in Artificial Intelligence, Berlin/Heidelberg, February 2009. Springer.
Ricard Gavaldà, Gábor Lugosi, Thomas Zeugmann, and Sandra Zilles, editors. Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 2009, Proceedings, volume 5809 of Lecture Notes in Artificial Intelligence, Berlin/Heidelberg, October 2009. Springer.
Osamu Watanabe and Thomas Zeugmann, editors. Stochastic Algorithms: Foundations and Applications, 5th International Symposium, SAGA 2009, Sapporo, Japan, October 2009, Proceedings, volume 5792 of Lecture Notes in Computer Science, Berlin/Heidelberg, October 2009. Springer.
International Conferences
Frank J. Balbach and Thomas Zeugmann. Recent developments in algorithmic teaching. In Adrian Horia Dediu, Armand Mihai Ionescu, and Carlos Martín-Vide, editors, Language and Automata Theory and Applications, Third International Conference, LATA 2009, Tarragona, Spain, April 2-8, 2009, Proceedings, volume 5457 of Lecture Notes in Computer Science, pages 1-18. Springer, 2009. Invited Paper.
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, pages 56-67, 2009.
Charles Jordan and Thomas Zeugmann. Relational properties expressible with one universal quantifier are testable. In Osamu Watanabe and Thomas Zeugmann, editors, Stochastic Algorithms: Foundations and Applications, 5th International Symposium, SAGA 2009, Sapporo, Japan, October 2009, Proceedings, volume 5792 of Lecture Notes in Computer Science, pages 141-155. Springer, 2009.
Ryo Yoshinaka. Learning mildly context-sensitive languages with multidimensional substitutability from positive data. In Ricard Gavaldà, Gábor Lugosi, Thomas Zeugmann, and Sandra Zilles, editors, Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 2009, Proceedings, volume 5809 of Lecture Notes in Artificial Intelligence, pages 278-292. Springer, 2009.
Technical Reports
Charles Harold Jordan. Master's thesis: The classification problem in relational property testing. Technical Report TCS-TR-B-09-6, Division of Computer Science, Hokkaido University, 2009.
Charles Jordan and Thomas Zeugmann. Contributions to the classification for testability: Four universal and one existential quantifier. Technical Report TCS-TR-A-09-39, Division of Computer Science, Hokkaido University, 2009.
Shin-ichi Minato and Takeaki Uno. Distinctive frequent itemset mining from time segmented databases using ZDD-based symbolic processing. Technical Report TCS-TR-A-09-37, Division of Computer Science, Hokkaido University, 2009.
2008
Journals
Yohji Akama and Thomas Zeugmann. Consistent and coherent learning with δ-delay. Information and Computation, 206 no. 11 pp. 1362-1374, 2008.
Haruya Iwasaki, Shin-ichi Minato, and Thomas Zeugmann. A method of ZBDD variable ordering for frequent pattern mining. The IEICE Transactions on Information and Systems (Japanese Edition), J91-D no. 3 pp. 608-618, 2008. in Japanese.
Shigeru Yamashita, Shin-ichi Minato, and D. Michael Miller. DDMF: An efficient decision diagram structure for design verification of quantum circuits under a practical restriction. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, E91-A no. 12 pp. 3793-3802, 2008.
Thomas Zeugmann and Sandra Zilles. Learning recursive functions: A survey. Theoret. Comput. Sci., 397 no. 1-3 pp. 4-56, 2008. Special issue Forty Years of Inductive Inference: Dedicated to the 60th Birthday of Rolf Wiehagen.
Steffen Lange, Thomas Zeugmann, and Sandra Zilles. Learning indexed families of recursive languages from positive data: A survey. Theoret. Comput. Sci., 397 no. 1-3 pp. 194-232, 2008. Special issue Forty Years of Inductive Inference: Dedicated to the 60th Birthday of Rolf Wiehagen.
Editorial Work
John Case, Takeshi Shinohara, Thomas Zeugmann, and Sandra Zilles. Foreword. Theoret. Comput. Sci., 397 no. 1-3 pp. 1-3, 2008. Special issue Forty Years of Inductive Inference: Dedicated to the 60th Birthday of Rolf Wiehagen.
Yoav Freund, László Györfi, György Turán, and Thomas Zeugmann, editors. Algorithmic Learning Theory, 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings, volume 5254 of Lecture Notes in Artificial Intelligence, Berlin/Heidelberg, October 2008. Springer.
International Conferences
Skip Jordan and Thomas Zeugmann. Indistinguishability and first-order logic. In Manindra Agrawal, Dingzhu Du, Zhenhua Duan, and Angsheng Li, editors, Theory and Applications of Models of Computation, 5th International Conference, TAMC 2008, Xi'an, China, April 2008, Proceedings, volume 4978 of Lecture Notes in Computer Science, pages 94-104. Springer, 2008.
Shin-ichi Minato, Takeaki Uno, and Hiroki Arimura. LCM over ZBDDs: Fast generation of very large-scale frequent itemsets using a compact graph-based representation. In Takashi Washio, Einoshin Suzuki, Kai Ming Ting, and Akihiro Inokuchi, editors, Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008, Proceedings, volume 5012 of Lecture Notes in Artificial Intelligence, pages 234-246. Springer, 2008.
S. Minato. A fast algorithm for cofactor implication checking and its application for knowledge discovery. In 8th IEEE International Conference on Computer and Information Technology (CIT 2008), pages 53-58. IEEE Computer Society, 2008.
S. Yamashita, S. Minato, and D. M. Miller. An efficient verification of quantum circuits under a practical restriction. In 8th IEEE International Conference on Computer and Information Technology (CIT 2008), pages 873-879. IEEE Computer Society, 2008.
Ryo Yoshinaka. An efficient algorithm for the inclusion problem of a subclass of DPDAs. In Carlos Martín-Vide, Friedrich Otto, and Henning Fernau, editors, Language and Automata Theory and Applications, Second International Conference, LATA 2008, Tarragona, Spain, March 13-19, 2008. Revised Papers, volume 5196 of Lecture Notes in Artificial Intelligence, pages 487-498. Springer, 2008.
Ryo Yoshinaka. Identification in the limit of k,l-substitutable context-free languages. In Alexander Clark, François Coste, and Laurent Miclet, editors, Grammatical Inference: Algorithms and Applications, 9th International Colloquium, ICGI 2008 Saint-Malo, France, September 22-24, 2008 Proceedings, volume 5278 of Lecture Notes in Artificial Intelligence, pages 266-279. Springer, 2008.
Local Conferences and Workshops
Takao Saitoh, Shin-ichi Minato, and Thomas Zeugmann (齋藤高央, 湊真一, ツォイクマントーマス). コルモゴロフ複雑性に基づく画像圧縮と分類に関する実験と考察 (Image compression and clustering based on Kolmogorov complexity). In FIT-2008 IEICE/IPSJ 第7回情報科学技術フォーラム, F-020, pages 355-357, 2008.
Technical Reports
Masakazu Ishihata, Yoshitaka Kameya, Taisuke Sato, and Shin-ichi Minato. Propositionalizing the EM algorithm by BDDs. Technical Report TR08-0004, Department of Computer Science, Tokyo Institute of Technology, jun 2008.
Haruya Iwasaki. Master thesis: Studies on variable ordering of zero-suppressed binary decision diagrams for database analysis. Technical Report TCS-TR-B-08-3, Department of Computer Science, Tokyo Institute of Technology, 2008. in Japanese.
Shane Legg, Jan Poland, and Thomas Zeugmann. On the limits of learning with computational models. Technical Report TCS-TR-A-08-34, Division of Computer Science, Hokkaido University, jan 2008.
Thomas Zeugmann. Course notes on complexity and cryptography. Technical Report TCS-TR-B-08-4, Division of Computer Science, Hokkaido University, 2008.
2007
Journals
Shin-ichi Minato and Hiroki Arimura. Frequent closed item set mining based on zero-suppressed BDDs. Transactions of the Japanese Society for Artificial Intelligence, 22 no. 2 pp. 165-172, 2007. Special Issue: Data Mining and Statistical Science.
Shin-ichi Minato and Kimihito Ito. Symmetric item set mining method using zero-suppressed BDDs and application to biological data. Transactions of the Japanese Society for Artificial Intelligence, 22 no. 2 pp. 156-164, 2007. Special Issue: Data Mining and Statistical Science.
Editorial Work
Shai Ben-David, John Case, and Thomas Zeugmann. Foreword. Theoret. Comput. Sci., 382 no. 3 pp. 167-169, 2007. Special issue for ALT 2004.
International Conferences
Yohji Akama and Thomas Zeugmann. Consistency conditions for inductive inference of recursive functions. In Takashi Washio, Ken Satoh, Hideaki Takeda, and Akihiro Inokuchi, editors, New Frontiers in Artificial Intelligence, JSAI 2006 Conference and Workshops, Tokyo, Japan, June 2006, Revised Selected Papers, volume 4384 of Lecture Notes in Artificial Intelligence, pages 251-264, Berlin, 2007. Springer.
Philippe de Groote, Sarah Maarek, and Ryo Yoshinaka. On two extensions of abstract categorial grammars. In Logic for Programming, Artificial Intelligence, and Reasoning, 14th International Conference, LPAR 2007. Yerevan, Armenia, October 2007. Proceedings, volume 4790 of Lecture Notes in Artificial Intelligence, pages 273-287. Springer, 2007.
Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann. N-gram analysis based on zero-suppressed BDDs. In Takashi Washio, Ken Satoh, Hideaki Takeda, and Akihiro Inokuchi, editors, New Frontiers in Artificial Intelligence, JSAI 2006 Conference and Workshops, Tokyo, Japan, June 2006, Revised Selected Papers, volume 4384 of Lecture Notes in Artificial Intelligence, pages 289-300, Berlin, 2007. Springer.
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, pages 85-90. IEEE, 2007.
Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann. Unordered N-gram representation based on zero-suppressed BDDs for text mining and classification. In Akihiro Yamamoto and Kouichi Hirata, editors, 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, pages 32-38. JSAI, 2007.
Shin-ichi Minato. A theoretical study on variable ordering of zero-suppressed BDDs for representing frequent itemsets. In Discovery Science, 10th International Conference, DS 2007 Sendai, Japan, October 1-4, 2007, Proceedings, volume 4755 of Lecture Notes in Artificial Intelligence, pages 139-150, Berlin, 2007. Springer.
Shin-ichi Minato, Ken Satoh, and Taisuke Sato. Compiling bayesian networks by symbolic probability calculation based on zero-suppressed bdds. In Proceedings of 20th International Joint Conference of Artificial Intelligence (IJCAI-2007), pages 2550-2555, 2007.
Ryo Yoshinaka. Learning efficiency of very simple grammars from positive data. In Marcus Hutter, Rocco A. Servedio, and Eiji Takimoto, editors, Algorithmic Learning Theory, 18th International Conference, ALT 2007, Sendai, Japan, October 2007, Proceedings, volume 4754 of Lecture Notes in Artificial Intelligence, pages 227-241, Berlin, oct 2007. Springer.
Local Conferences and Workshops
Technical Reports
Yohji Akama and Thomas Zeugmann. Consistent and coherent learning with δ-delay. Technical Report TCS-TR-A-07-29, Division of Computer Science, Hokkaido University, 2007.
Steffen Lange, Thomas Zeugmann, and Sandra Zilles. Learning indexed families of recursive languages from positive data. Technical Report TCS-TR-A-07-31, Division of Computer Science, Hokkaido University, 2007.
Shin-ichi Minato. A theoretical study on variable ordering of zbdds for representing frequent itemsets. Technical Report TCS-TR-A-07-27, Division of Computer Science, Hokkaido University, 2007.
Shin-ichi Minato and Nicolas Spyratos. Keyword query processing using binary decision diagrams under a taxonomy model. Technical Report TCS-TR-A-07-28, Division of Computer Science, Hokkaido University, 2007.
Shin-ichi Minato, Takeaki Uno, and Hiroki Arimura. Fast generation of very large-scale frequent itemsets using a compact graph-based representation. Technical Report TCS-TR-A-07-30, Division of Computer Science, Hokkaido University, 2007.
Shigeru Yamashita, Shin-ichi Minato, and D. Michael Miller. An efficient decision diagram structure for design verification of quantum circuits under a practical restriction. Technical Report TCS-TR-A-07-33, Division of Computer Science, Hokkaido University, dec 2007.
Thomas Zeugmann. Course notes on theory of computation. Technical Report TCS-TR-B-07-2, Division of Computer Science, Hokkaido University, 2007.
Thomas Zeugmann and Sandra Zilles. Learning recursive functions. Technical Report TCS-TR-A-07-32, Division of Computer Science, Hokkaido University, nov 2007.
2006
Journals
Jan Poland and Marcus Hutter. MDL convergence speed for Bernoulli sequences. Statistics and Computing, 16 no. 2 pp. 161-175, 2006.
Thomas Zeugmann. From learning in the limit to stochastic finite learning. Theoret. Comput. Sci., 364 no. 1 pp. 77-97, 2006. Special issue for ALT 2003.
John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann. Learning a subclass of regular patterns in polynomial time. Theoret. Comput. Sci., 364 no. 1 pp. 115-131, 2006. Special issue for ALT 2003.
Editorial Work
Nicolò Cesa-Bianchi, Rüdiger Reischuk, and Thomas Zeugmann. Foreword. Theoret. Comput. Sci., 350 no. 1 pp. 1-2, 2006. Special issue for ALT 2002.
International Conferences
Frank J. Balbach and Thomas Zeugmann. Teaching randomized learners. In Gabor Lugosi and Hans Ulrich Simon, editors, Learning Theory: 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 2006, Proceedings, volume 4005 of Lecture Notes in Artificial Intelligence, pages 229-243, Berlin, 2006. Springer.
Frank J. Balbach and Thomas Zeugmann. Teaching memoryless randomized learners without feedback. In José L. Balcázar, Philip M. Long, and Frank Stephan, editors, Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 2006, Proceedings, volume 4264 of Lecture Notes in Artificial Intelligence, pages 93-108. Springer, October 2006.
Björn Hoffmeister and Thomas Zeugmann. Text mining using markov chains of variable length. In Klaus P. Jantke, Aran Lunzer, Nicolas Spyratos, and Yuzuru Tanaka, editors, Federation over the Web: International Workshop, Dagstuhl Castle, Germany, May 1-6, 2005. Revised Selected Papers, volume 3847 of Lecture Notes in Artificial Intelligence, pages 1-24, Berlin, 2006. Springer.
Jan Poland. The missing consistency theorem for Bayesian learning: Stochastic model selection. In José L. Balcázar, Philip M. Long, and Frank Stephan, editors, Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 2006, Proceedings, volume 4264 of Lecture Notes in Artificial Intelligence, pages 259-273. Springer, October 2006.
Shin-ichi Minato. Symmetric item set mining based on zero-suppressed BDDs. In Ljupčo Todorovski, Nada Lavrač, and Klaus P. Jantke, editors, Discovery Science, 9th International Conference, DS 2006, Barcelona, Spain, October 2006, Proceedings, volume 4265 of Lecture Notes in Artificial Intelligence, pages 321-326. Springer, October 2006.
Jan Poland and Thomas Zeugmann. Clustering pairwise distances with missing data: Maximum cuts versus normalized cuts. In Ljupčo Todorovski, Nada Lavrač, and Klaus P. Jantke, editors, Discovery Science, 9th International Conference, DS 2006, Barcelona, Spain, October 2006, Proceedings, volume 4265 of Lecture Notes in Artificial Intelligence, pages 197-208. Springer, October 2006.
Takeshi Shibata, Ryo Yoshinaka, and Takashi Chikayama. Probabilistic generalization of simple grammars and its application to reinforcement learning. In José L. Balcázar, Philip M. Long, and Frank Stephan, editors, Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 2006, Proceedings, volume 4264 of Lecture Notes in Artificial Intelligence, pages 348-362. Springer, October 2006.
Ryo Yoshinaka. Linearization of affine abstract categorial grammars. In Shuly Wintner, editor, Proceedings of FG 2006: The 11th Conference on Formal Grammar, Malaga, Spain, July 29-30, 2006, pages 185-199, Stanford, CA, USA, February 2007. CSLI Publications.
Ryo Yoshinaka. Polynomial-time identification of an extension of very simple grammars from positive data. In Grammatical Inference: Algorithms and Applications, 8th International Colloquium, ICGI 2006, Tokyo, Japan, September 20-22, 2006. Proceedings, volume 4201 of Lecture Notes in Artificial Intelligence, pages 45-58. Springer, September 2006.
Thomas Zeugmann. Inductive inference and language learning. In Jin-Yi Cai, S. Barry Cooper, and Angsheng Li, editors, Theory and Applications of Models of Computation, Third International Conference, TAMC 2006, Beijing, China, May 2006, Proceedings, volume 3959 of Lecture Notes in Computer Science, pages 464-473. Springer, may 2006. Invited Paper.
Local Conferences and Workshops
岩崎 玄弥, 湊 真一, and 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. In 人工知能学会 第63回 人工知能基本問題研究会, SIG-FPAI-A601, pages 65-70, 2006.
Jan Poland and Thomas Zeugmann. Clustering based on graph cuts. In 人工知能学会 第63回 人工知能基本問題研究会, SIG-FPAI-A601, pages 77-82, 2006.
Jan Poland and Thomas Zeugmann. Clustering the google distance with eigenvectors and semidefinite programming. In Knowledge Media Technologies, First International Core-to-Core Workshop, volume 21 of Diskussionsbeiträge, Institut für Medien und Kommunikationswisschaft, pages 61-69. Technische Universität Ilmenau, 2006.
Technical Reports
Frank J. Balbach and Thomas Zeugmann. On the teachability of randomized learners. Technical Report TCS-TR-A-06-13, Division of Computer Science, Hokkaido University, April 2006.
Ryutaro Kurai, Shin-ichi Minato, and Thomas Zeugmann. N-gram analysis based on zero-suppressed BDDs. Technical Report TCS-TR-A-06-16, Division of Computer Science, Hokkaido University, 2006.
Shin-ichi Minato. Symmetric item set mining using zero-suppressed BDDs. Technical Report TCS-TR-A-06-14, Division of Computer Science, Hokkaido University, 2006.
Shin-ichi Minato. Generating frequent closed item sets based on zero-suppressed BDDs. Technical Report TCS-TR-A-06-17, Division of Computer Science, Hokkaido University, 2006.
Shin-ichi Minato and Kimihito Ito. Symmetric item set mining method using ZBDDs and application to biological data. Technical Report TCS-TR-A-06-22, Division of Computer Science, Hokkaido University, 2006.
Shin-ichi Minato, Ken Satoh, and Taisuke Sato. Compiling bayesian networks by symbolic probability calculation using zero-suppressed BDDs. Technical Report TCS-TR-A-06-18, Division of Computer Science, Hokkaido University, 2006.
Shin-ichi Minato, Hirokazu Takahashi, Takeru Inoue, Hiroshi Tohjo, and Kan Toyoshima. A framework of programmable multicast applications using flexcast and java applet. Technical Report TCS-TR-A-06-10, Division of Computer Science, Hokkaido University, 2006.
Shin-ichi Minato and Hiroki Arimura. ZBDD-growth: An efficient method for frequent pattern mining and knowledge indexing. Technical Report TCS-TR-A-06-12, Division of Computer Science, Hokkaido University, 2006.
Jan Poland. Potential functions for stochastic model selection. Technical Report TCS-TR-A-06-11, Division of Computer Science, Hokkaido University, 2006.
2005
Journals
Steffen Lange, Gunter Grieser, and Thomas Zeugmann. Inductive inference of approximations for recursive concepts. Theoret. Comput. Sci., 348 no. 1 pp. 15-40, 2005. Special issue for ALT 2000.
Jan Poland and Marcus Hutter. Asymptotics of discrete MDL for online prediction. IEEE Transactions on Information Theory, 51 no. 11 pp. 3780-3795, 2005.
Marcus Hutter and Jan Poland. Adaptive online prediction by following the perturbed leader. Journal of Machine Learning Research, 6 pp. 639-660, 2005.
International Conferences
Frank J. Balbach and Thomas Zeugmann. Teaching learners with restricted mind changes. In Sanjay Jain, Hans Ulrich Simon, and Etsuji Tomita, editors, Algorithmic Learning Theory, 16th International Conference, ALT 2005, Singapore, October 2005, Proceedings, volume 3734 of Lecture Notes in Artificial Intelligence, pages 474-489. Springer, October 2005.
Jan Poland. FPL analysis for adaptive bandits. In Stochastic Algorithms: Foundations and Applications, Third International Symposium, SAGA 2005, Moscow, Russia, October 20-22, 2005. Proceedings, volume 3777 of Lecture Notes in Computer Science, pages 58-69. Springer, 2005.
Jan Poland and Marcus Hutter. Defensive universal learning with experts. In Sanjay Jain, Hans Ulrich Simon, and Etsuji Tomita, editors, Algorithmic Learning Theory, 16th International Conference, ALT 2005, Singapore, October 2005, Proceedings, volume 3734 of Lecture Notes in Artificial Intelligence, pages 356-370, Berlin, 2005. Springer. Technical Report Version.
Local Conferences and Workshops
J. Poland and M. Hutter. Master algorithms for active experts problems based on increasing loss values, 2005. Presented at the Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn).
J. Poland and M. Hutter. Strong asymptotic assertions for discrete MDL in regression and classification, 2005. Presented at the Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn).
I. Fischer and J. Poland. Amplifying the block matrix structure for spectral clustering, 2005. Presented at the Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn).
Technical Reports
Frank J. Balbach and Thomas Zeugmann. Teaching learners that can only perform restricted mind changes. Technical Report TCS-TR-A-05-5, Division of Computer Science, Hokkaido University, 2005.
Shin-ichi Minato. VSOP (valued-sum-of-products) calculator based on zero-suppressed BDDs. Technical Report TCS-TR-A-05-3, Division of Computer Science, Hokkaido University, 2005.
Shin-ichi Minato. Finding all simple disjoint decompositions in frequent itemset data. Technical Report TCS-TR-A-05-9, Division of Computer Science, Hokkaido University, 2005.
Jan Poland. FPL analysis for adaptive bandits. Technical Report TCS-TR-A-05-7, Division of Computer Science, Hokkaido University, 2005.
Jan Poland and Marcus Hutter. Defensive universal learning with experts. Technical Report TCS-TR-A-05-4, Division of Computer Science, Hokkaido University, 2005.
Thomas Zeugmann. From learning in the limit to stochastic finite learning. Technical Report TCS-TR-A-05-8, Division of Computer Science, Hokkaido University, August 2005.
2004
Journals
Jan Poland. A coding theorem for enumerable output machines. Information Processing Letters, 91 no. 4 pp. 157-161, 2004.
International Conferences
Technical Reports
John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann. A polynomial time learner for a subclass of regular patterns. Technical Report Report TR04-038, Electronic Colloquium on Computational Complexity, April 2004.
Shin-ichi Minato and Hiroki Arimura. Combinatorial item set analysis based on zero-suppressed BDDs. Technical Report TCS-TR-A-04-1, Division of Computer Science, Hokkaido University, 2004.
2003
Journals
Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas Zeugmann. On learning of functions refutably. Theoret. Comput. Sci., 298 no. 1 pp. 111-143, 2003.
Seiichiro Tani, Takeru Inoue, Shin-ichi Minato, Hirokazu Takahashi, Satoshi Kotabe, and Toshiaki Miyazaki. Global multi-point streaming experiments based on the flexcast protocol. NTT Technical Review, 1 no. 5 pp. 24-30, 2003.
Editorial Work
Takeshi Shinohara, Carl H. Smith, and Thomas Zeugmann. Foreword. Theoret. Comput. Sci., 298 no. 1 pp. 1-4, 2003.
International Conferences
John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann. Learning a subclass of regular patterns in polynomial time. In Ricard Gavaldà, Klaus P. Jantke, and Eiji Takimoto, editors, Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 2003, Proceedings, volume 2842 of Lecture Notes in Artificial Intelligence, pages 234-246. Springer, 2003.
Thomas Zeugmann. Can learning in the limit be done efficiently?. In Ricard Gavaldà, Klaus P. Jantke, and Eiji Takimoto, editors, Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 2003, Proceedings, volume 2842 of Lecture Notes in Artificial Intelligence, pages 17-38. Springer, 2003. Invited Paper.
Technical Reports
2002
Journals
Shin-ichi Minato. Streaming BDD manipulation. IEEE Transactions on Computers, 51 no. 5 pp. 474-485, 2002.
Frank Stephan and Thomas Zeugmann. Learning classes of approximations to non-recursive functions. Theoret. Comput. Sci., 288 no. 2 pp. 309-341, 2002. Special issue for ALT '99.
International Conferences
Technical Reports
2001
Journals
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. Theoret. Comput. Sci., 261 no. 1 pp. 119-156, 2001. Special issue for ALT '97.
Shin-ichi Minato. Zero-suppressed BDDs and their applications. International Journal on Software Tools for Technology Transfer, 3 no. 2 pp. 156-170, 2001.
Peter Rossmanith and Thomas Zeugmann. Stochastic finite learning of the pattern languages. Machine Learning, 44 no. 1/2 pp. 67-91, 2001.
Rolf Wiehagen and Thomas Zeugmann. Foreword. Theoret. Comput. Sci., 268 no. 2 pp. 175-177, 2001. Special issue for ALT '98.
International Conferences
Sanjay Jain, Efim Kinber, Rolf Wiehagen, and Thomas Zeugmann. Learning recursive functions refutably. In Naoki Abe, Roni Khardon, and Thomas Zeugmann, editors, Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings, volume 2225 of Lecture Notes in Artificial Intelligence, pages 283-298. Springer, 2001.
Thomas Zeugmann. Stochastic finite learning. In Kathleen Steinhöfel, editor, Stochastic Algorithms: Foundations and Applications, International Symposium, SAGA 2001, Berlin, Germany, December 13-14, 2001, Proceedings, volume 2264 of Lecture Notes in Computer Science, pages 155-171. Springer, 2001. Invited Paper.
Editorial Work
Naoki Abe, Roni Khardon, and Thomas Zeugmann, editors. Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings, volume 2225 of Lecture Notes in Artificial Intelligence, Berlin, November 2001. Springer.
Naoki Abe, Roni Khardon, and Thomas Zeugmann. Editors' introduction. In Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings, volume 2225 of Lecture Notes in Artificial Intelligence, pages 1-7. Springer, 2001.
Rolf Wiehagen and Thomas Zeugmann. Foreword. Theoret. Comput. Sci., 268 no. 2 pp. 175-177, 2001. Special issue for ALT '98.
Technical Reports
S. Lange, G. Grieser, and T. Zeugmann. Learning approximations of recursive concepts. Technical Report SIIM-TR-A-01-03, Schriftenreihe der Institute für Informatik/Mathematik, Medizinische Universität zu Lübeck, 2001.
S. Jain, E. Kinber, R. Wiehagen, and T. Zeugmann. Refutable inductive inference of recursive functions. Technical Report SIIM-TR-A-01-06, Schriftenreihe der Institute für Informatik/Mathematik, Medizinische Universität zu Lübeck, 2001.
2000
Journals
Sanjay Jain, Efim Kinber, Steffen Lange, Rolf Wiehagen, and Thomas Zeugmann. Learning languages and functions by erasing. Theoret. Comput. Sci., 241 no. 1-2 pp. 143-189, 2000. Special issue for ALT '96.
Rüdiger Reischuk and Thomas Zeugmann. An average-case optimal one-variable pattern language learner. J. Comput. Syst. Sci., 60 no. 2 pp. 302-335, 2000. Special Issue for COLT '98.
International Conferences
Gunter Grieser, Steffen Lange, and Thomas Zeugmann. Learning recursive concepts with anomalies. In Hiroki Arimura, Sanjay Jain, and Arun Sharma, editors, Algorithmic Learning Theory, 11th International Conference, ALT 2000, Sydney, Australia, December 2000, Proceedings, volume 1968 of Lecture Notes in Artificial Intelligence, pages 101-115. Springer, 2000.
Frank Stephan and Thomas Zeugmann. Average-case complexity of learning polynomials. In Nicolò Cesa-Bianchi and Sally Goldman, editors, Proc. 13th Annu. Conference on Comput. Learning Theory, pages 59-68. Morgan Kaufmann, San Francisco, 2000.
Technical Reports
1999
Journals
John Case, Sanjay Jain, Steffen Lange, and Thomas Zeugmann. Incremental concept learning for bounded data mining. Inform. Comput., 152 no. 1 pp. 74-110, 1999.
International Conferences
Rüdiger Reischuk and Thomas Zeugmann. A complete and tight average-case analysis of learning monomials. In Christoph Meinel and Sophie Tison, editors, STACS 99, 16th Annual Symposium on Theoretical Aspects of Computer Science, Trier, Germany, March 1999, Proceedings, volume 1563 of Lecture Notes in Computer Science, pages 414-423. Springer, 1999.
Frank Stephan and Thomas Zeugmann. On the uniform learnability of approximations to non-recursive functions. In Osamu Watanabe and Takashi Yokomori, editors, Algorithmic Learning Theory, 10th International Conference, ALT '99, Tokyo, Japan, December 1999, Proceedings, volume 1720 of Lecture Notes in Artificial Intelligence, pages 276-290. Springer, 1999.
Technical Reports
Frank Stephan and Thomas Zeugmann. Learning classes of approximations to non-recursive functions. Technical Report DOI-TR-166, Department of Informatics, Kyushu University, 1999.
1998
Journals
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, 23 pp. 117-145, 1998. Special issue for AII '94 and ALT '94.
International Conferences
Rüdiger Reischuk and Thomas Zeugmann. Learning one-variable pattern languages in linear average time. In Proc. 11th Annu. Conf. on Comput. Learning Theory, pages 198-208, New York, NY, 1998. ACM Press.
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, volume 1433 of Lecture Notes in Artificial Intelligence, pages 13-24. Springer, 1998.
Editorial Work
Michael M. Richter, Carl H. Smith, Rolf Wiehagen, and Thomas Zeugmann, editors. Algorithmic Learning Theory, 9th International Conference, ALT '98, Otzenhausen, Germany, October 1998, Proceedings, volume 1501 of Lecture Notes in Artificial Intelligence. Springer-Verlag, October 1998.
Michael M. Richter, Carl H. Smith, Rolf Wiehagen, and Thomas Zeugmann. Editors' introduction. In Algorithmic Learning Theory, 9th International Conference, ALT '98, Otzenhausen, Germany, October 1998, Proceedings, volume 1501 of Lecture Notes in Artificial Intelligence, pages 1-10. Springer, 1998.
Technical Reports
Rüdiger Reischuk and Thomas Zeugmann. Analyzing the average-case behavior of conjunctive learning algorithms. Technical Report DOI-TR-153, Department of Informatics, Kyushu University, 1998.
Peter Rossmanith and Thomas Zeugmann. Learning k-variable pattern languages efficiently stochastically finite on average from positive data. Technical Report DOI-TR-145, Department of Informatics, Kyushu University, 1998.
Rüdiger Reischuk and Thomas Zeugmann. An average-case optimal one-variable pattern language learner. Technical Report Report TR98-069, Electronic Colloquium on Computational Complexity, 1998.
1997
Journals
D. Rotter, K. Hamaguchi, S. Minato, and S. Yajima. Manipulation of large-scale polynomials using BMDs. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, E80-A no. 10 pp. 1774-1781, 1997.
Shin-Ichi Minato. Arithmetic boolean expression manipulator using BDDs. Formal Methods in System Design, 10 no. 2/3 pp. 221-242, 1997.
Carl H. Smith, Rolf Wiehagen, and Thomas Zeugmann. Classifying predicates and languages. International Journal of Foundations of Computer Science, 8 no. 1 pp. 15-41, 1997.
Editorial Work
T. Zeugmann. Foreword. Theoret. Comput. Sci., 185 no. 1 pp. 1-1, 1997. Special issue for ALT '95.
International Conferences
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, volume 1316 of Lecture Notes in Artificial Intelligence, pages 260-276. Springer, 1997.
Technical Reports
John Case, Sanjay Jain, Steffen Lange, and Thomas Zeugmann. Incremental concept learning for bounded data mining. Technical Report DOI-TR-136, Department of Informatics, Kyushu University, 1997.
Rüdiger Reischuk and Thomas Zeugmann. Learning one-variable pattern languages in linear average time. Technical Report DOI-TR-140, Department of Informatics, Kyushu University, 1997.
1996
Journals
Steffen Lange and Thomas Zeugmann. Incremental learning from positive data. J. of Comput. Syst. Sci., 53 no. 1 pp. 88-103, 1996.
S. Lange and T. Zeugmann. Set-driven and rearrangement-independent learning of recursive languages. Math. Syst. Theory, 29 no. 6 pp. 599-634, 1996.
Steffen Lange, Thomas Zeugmann, and Shyam Kapur. Monotonic and dual monotonic language learning. Theoret. Comput. Sci., 155 no. 2 pp. 365-410, 1996.
S. Minato. Fast factorization method for implicit cube set representation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 15 no. 4 pp. 377-384, 1996.
International Conferences
Rusins Freivalds and Thomas Zeugmann. Co-learning of recursive languages from positive data. In Dines Bjørner, Manfred Broy, and Igor V. Pottosin, editors, Perspectives of System Informatics, Second International Andrei Ershov Memorial Conference, Akademgorodok, Novosibirsk, Russia, June 1996, Proceedings, volume 1181 of Lecture Notes in Computer Science, pages 122-133. Springer, 1996.
Steffen Lange, Rolf Wiehagen, and Thomas Zeugmann. Learning by erasing. In Setsuo Arikawa and Arun K. Sharma, editors, Algorithmic Learning Theory, 7th International Workshop, ALT '96, Sydney, Australia, October 1996, Proceedings, volume 1160 of Lecture Notes in Artificial Intelligence, pages 228-241. Springer, 1996.
Technical Reports
Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, and Thomas Zeugmann. Efficient learning of one-variable pattern languages from positive examples. Technical Report DOI-TR-128, Department of Informatics, Kyushu University, 1996.
Rolf Wiehagen, Steffen Lange, and Thomas Zeugmann. Learning by erasing. Technical Report RIFIS-TR-CS-122, RIFIS, Kyushu University 33, 1996.
1995
Journals
William I. Gasarch, Efim B. Kinber, Mark G. Pleszkoch, Carl H. Smith, and Thomas Zeugmann. Learning via queries, teams, and anomalies. Fundamenta Informaticae, 23 pp. 67-89, 1995.
Steffen Lange and Thomas Zeugmann. Trading monotonicity demands versus efficiency. Bulletin of Informatics and Cybernetics, 27 no. 1 pp. 53-83, 1995.
Thomas Zeugmann, Steffen Lange, and Shyam Kapur. Characterizations of monotonic and dual monotonic language learning. Inform. Comput., 120 no. 2 pp. 155-173, 1995.
International Conferences
Steffen Lange and Thomas Zeugmann. Refined incremental learning. In Xin Yao, editor, Proc. 8th Australian Joint Conference on Artificial Intelligence - AI'95, pages 147-154. World Scientific Publ. Co., 1995.
Steffen Lange and Thomas Zeugmann. Trading monotonicity demands versus mind changes. In Paul Vitányi, editor, Computational Learning Theory, Second European Conference, EuroCOLT '95, Barcelona, Spain, March 1995, Proceedings, volume 904 of Lecture Notes in Artificial Intelligence, pages 125-139. Springer, 1995.
Editorial Work
Klaus P. Jantke, Takeshi Shinohara, and Thomas Zeugmann, editors. Algorithmic Learning Theory, 6th International Workshop, ALT '95, Fukuoka, Japan, October 18-20, 1995, Proceedings, volume 997 of Lecture Notes in Artificial Intelligence, Berlin, October 1995. Springer.
Klaus P. Jantke, Takeshi Shinohara, and T. Zeugmann. Editors' introduction. In Algorithmic Learning Theory, 6th International Workshop, ALT '95, Fukuoka, Japan, October 18-20, 1995, Proceedings, volume 997 of Lecture Notes in Artificial Intelligence, pages ix-xv. Springer, 1995.
Book Chapters
Rolf Wiehagen, Carl H. Smith, and Thomas Zeugmann. Classifying recursive predicates and languages. In Algorithmic Learning for Knowledge-Based Systems, volume 961 of Lecture Notes in Artificial Intelligence, pages 174-189. Springer, 1995.
Rolf Wiehagen and Thomas Zeugmann. Learning and consistency. In Algorithmic Learning for Knowledge-Based Systems, volume 961 of Lecture Notes in Artificial Intelligence, pages 1-24. Springer, 1995.
Thomas Zeugmann and Steffen Lange. A guided tour across the boundaries of learning recursive languages. In Algorithmic Learning for Knowledge-Based Systems, volume 961 of Lecture Notes in Artificial Intelligence, pages 190-258. Springer, 1995.
Technical Reports
Rusins Freivalds and Thomas Zeugmann. Co-learning of recursive languages from positive data. Technical Report RIFIS-TR-CS-110, RIFIS, Kyushu University 33, 1995.
Steffen Lange and Thomas Zeugmann. Modeling incremental learning from positive data. Technical Report RIFIS-TR-CS-117, RIFIS, Kyushu University 33, 1995.
Takashi Tabe and Thomas Zeugmann. Two variations of inductive inference of languages from positive data. Technical Report RIFIS-TR-CS-105, RIFIS, Kyushu University 33, 1995.
Thomas Zeugmann. Lange and wiehagen's pattern language learning algorithm: An average-case analysis with respect to its total learning time. Technical Report RIFIS-TR-CS-111, RIFIS, Kyushu University 33, 1995.
1994
Journals
Steffen Lange and Thomas Zeugmann. Characterization of language learning from informant under various monotonicity constraints. J. of Experimental and Theoret. Artif. Intell., 6 no. 1 pp. 73-94, 1994. Special issue for AII '92.
Rolf Wiehagen and Thomas Zeugmann. Ignoring data may be the only way to learn efficiently. J. of Experimental and Theoret. Artif. Intell., 6 no. 1 pp. 131-144, 1994. Special issue for AII '92.
Thomas Zeugmann. Report on COLT 1994. ACM SIGART Bulletin, 5 no. 4 pp. 25-27, 1994.
Thomas Zeugmann. Report on COLT 1994. ACM SIGACT News, 25 no. 4 pp. 88-95, 1994.
International Conferences
Rolf Wiehagen, Carl H. Smith, and Thomas Zeugmann. Classification of predicates and languages. In Computational Learning Theory: EuroColt '93, volume New Series Number 53 of The Institute of Mathematics and its Applications Conference Series, pages 171-181, Oxford, 1994. Oxford University Press.
Steffen Lange and Thomas 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, volume 872 of Lecture Notes in Artificial Intelligence, pages 453-468. Springer-Verlag, 1994.
Thomas Zeugmann. Average-case analysis of pattern language learning algorithms. 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, volume 872 of Lecture Notes in Artificial Intelligence, pages 8-9. Springer-Verlag, 1994.
Technical Reports
1993
Journals
Steffen Lange and Thomas Zeugmann. Learning recursive languages with bounded mind changes. International Journal of Foundations of Computer Science, 4 no. 2 pp. 157-178, 1993.
S. Minato. Fast generation of prime-irredundant covers from binary decision diagrams. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, E76-A no. 6 pp. 967-973, 1993.
S. Minato. BEM-II: An arithmetic Boolean expression manipulator using BDDs. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, E76-A no. 10 pp. 1721-1729, 1993.
International Conferences
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, volume 665 of Lecture Notes in Computer Science, pages 682-691. Springer-Verlag, 1993.
Steffen Lange and Thomas Zeugmann. Language learning in dependence on the space of hypotheses. In Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, pages 127-136, New York, NY, 1993. ACM Press.
Steffen Lange and Thomas Zeugmann. Monotonic versus non-monotonic language learning. In Nonmonotonic and Inductive Logic, Second International Workshop, Reinhardsbrunn Castle, Germany, December 1991, volume 659 of Lecture Notes in Artificial Intelligence, pages 254-269. Springer-Verlag, 1993.
Technical Reports
Steffen Lange and Thomas Zeugmann. The learnability of recursive languages in dependence on the hypothesis space. Technical Report 20/93, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, 1993.
Steffen Lange and Thomas Zeugmann. On the impact of order independence to the learnability of recursive languages. Technical Report Research Report ISIS-RR-93-17E, FUJITSU Laboratories Ltd., Numazu, Japan, 1993.
1992
Journals
Thomas Zeugmann. Highly parallel computations modulo a number having only small prime factors. Inform. Comput., 96 no. 1 pp. 95-114, 1992.
S. Minato. Minimum-width method of variable ordering for binary decision diagrams. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, E75-A no. 3 pp. 392-399, 1992.
International Conferences
Steffen Lange and Thomas Zeugmann. Types of monotonic language learning and their characterization. In Proc. 5th Annual ACM Workshop on Comput. Learning Theory, pages 377-390, New York, NY, 1992. ACM Press.
Steffen Lange and Thomas Zeugmann. A unifying approach to monotonic language learning on informant. In Analogical and Inductive Inference, International Workshop AII '92, Dagstuhl Castle, Germany, October 1992, Proceedings, volume 642 of Lecture Notes in Artificial Intelligence, pages 244-259. Springer-Verlag, 1992.
Rolf Wiehagen and Thomas Zeugmann. Too much information can be too much for efficient learning. In Analogical and Inductive Inference, International Workshop AII '92, Dagstuhl Castle, Germany, October 1992, Proceedings, volume 642 of Lecture Notes in Artificial Intelligence, pages 72-86. Springer-Verlag, 1992. Invited Paper.
Technical Reports
Steffen Lange and Thomas Zeugmann. On the power of monotonic language learning. Technical Report 5/92, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, 1992.
Steffen Lange and Thomas Zeugmann. Learning recursive languages with bounded mind changes. Technical Report 16/92, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, 1992.
Steffen Lange, Thomas Zeugmann, and Shyam Kapur. Class preserving monotonic and dual monotonic language learning. Technical Report 14/92, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, 1992.
Rolf Wiehagen and Thomas Zeugmann. Inconsistency can be necessary for learning in polynomial time. Technical Report 13/92, GOSLER-Report, FB Mathematik und Informatik, TH Leipzig, 1992.
Thomas Zeugmann, Steffen Lange, and Shyam Kapur. Characterizations of class preserving monotonic and dual monotonic language learning. Technical Report Technical Report IRCS 92 - 24, Institute for Research in Cognitive Science, University of Pennsylvania, Philadelphia, 1992.
1991
Journals
Efim Kinber and Thomas Zeugmann. One-sided error probabilistic inductive inference and reliable frequency identification. Inform. Comput., 92 no. 2 pp. 253-284, 1991.
Technical Reports
International Conferences
1990
Journals
International Conferences
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 Theory, pages 327-337, San Mateo, CA, 1990. Morgan Kaufmann.
Thomas Zeugmann. Computing large polynomial powers very fast in parallel. In Mathematical Foundations of Computer Science 1990, Banská Bystrica, Czechoslovakia, August 1990, Proceedings, volume 452 of Lecture Notes in Computer Science, pages 538-544. Springer-Verlag, 1990.
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, volume 543 of Lecture Notes in Artificial Intelligence, pages 208-222. Springer-Verlag, 1990.
Book Chapters
Thomas Zeugmann. Parallel algorithms. In Encyclopedia of Computer Science and Technology, volume 21, Supplement 6, pages 223-244. Marcel Dekker Inc. New York and Basel, 1990.
Technical Reports
1989
Journals
Thomas Zeugmann. Improved parallel computations in the ring Zpα. Elektronische Informationsverarbeitung und Kybernetik, 25 no. 10 pp. 543-547, 1989.
International Conferences
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 1989, Proceedings, volume 380 of Lecture Notes in Computer Science, pages 257-266. Springer-Verlag, 1989.
Efim B. Kinber and Thomas Zeugmann. Refined query inference. In Analogical and Inductive Inference, International Workshop AII '89, Reinhardsbrunn Castle, GDR, October 1989, Proceedings, volume 397 of Lecture Notes in Artificial Intelligence, pages 148-160. Springer-Verlag, 1989.
Technical Reports
1988
Journals
Thomas Zeugmann. On the power of recursive optimizers. Theoret. Comput. Sci., 62 no. 3 pp. 289-310, 1988.
International Conferences
Technical Reports
Efim Kinber and Thomas Zeugmann. One-sided error probabilistic inductive inference and reliable frequency identification. Technical Report Preprint Nr. 185, Humboldt-Universität zu Berlin, Sektion Mathematik, 1988.
Thomas Zeugmann. Parallel algorithms. Technical Report Preprint Nr. 186, Humboldt-Universität zu Berlin, Sektion Mathematik, 1988.
1987
Journals
International Conferences
Technical Reports
1986
Journals
International Conferences
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, volume 215 of Lecture Notes in Computer Science, pages 240-245. Springer-Verlag, 1986.
Thomas Zeugmann. On Barzdin's conjecture. In Analogical and Inductive Inference, International Workshop AII '86. Wendisch-Rietz, GDR, October 1986, Proceedings, volume 265 of Lecture Notes in Computer Science, pages 220-227. Springer-Verlag, 1986.
Technical Reports
1985
Journals
Efim B. Kinber and Thomas Zeugmann. Inductive inference of almost everywhere correct programs by reliably working strategies. Elektronische Informationsverarbeitung und Kybernetik, 21 no. 3 pp. 91-100, 1985.
International Conferences
Technical Reports
1984
Journals
Thomas Zeugmann. On the nonboundability of total effective operators. Zeitschr. f. math. Logik und Grundlagen d. Math., 30 no. 9-11 pp. 169-172, 1984.
International Conferences
Technical Reports
1983
Journals
Thomas Zeugmann. A-posteriori characterizations in inductive inference of recursive functions. Elektronische Informationsverarbeitung und Kybernetik, 19 no. 10/11 pp. 559-594, 1983.
Thomas Zeugmann. On the synthesis of fastest programs in inductive inference. Elektronische Informationsverarbeitung und Kybernetik, 19 no. 12 pp. 625-642, 1983.
International Conferences
Technical Reports
Thomas Zeugmann. Zur algorithmischen Synthese von schnellen Programmen. PhD thesis, Humboldt-Universität zu Berlin, Sektion Mathematik, 1983.
International Conferences
Thomas Zeugmann. On the finite identification of fastest programs. In Helena Rasiowa and Helmut Thiele, editors, Symposium on Mathematical Foundations of Computer Science, December 6-11, 1982, Diedrichshagen, pages 151-159, Berlin, Germany, 1983. Seminarbericht Nr. 52, Sektion Mathematik, Humboldt-Universität zu Berlin.
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