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

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Hokkaido University

Publications that appeared as book chapters

  1. Thomas Zeugmann,
    Parallel Algorithms, Encyclopedia of Computer Science and Technology, Vol. 21, Supplement 6, Allen Kent and James G. Williams (Eds.), pp. 223 - 244, Marcel Dekker Inc. New York and Basel, 1990.
    Abstract
  2. R. Wiehagen and T. Zeugmann,
    Learning and Consistency,
    in “Algorithmic Learning for Knowledge-Based Systems,” (K.P. Jantke and S. Lange, Eds.), Lecture Notes in Artificial Intelligence 961, pp. 1 - 24, Springer-Verlag 1995.
    Abstract.
  3. R. Wiehagen, C.H. Smith and T. Zeugmann,
    Classifying recursive predicates and languages,
    in “Algorithmic Learning for Knowledge-Based Systems,” (K.P. Jantke and S. Lange, Eds.), Lecture Notes in Artificial Intelligence 961, pp. 174 - 189, Springer-Verlag 1995.
    Abstract.
  4. T. Zeugmann and S. Lange,
    A Guided Tour Across the Boundaries of Learning Recursive Languages,
    in “Algorithmic Learning for Knowledge-Based Systems,” (K.P. Jantke and S. Lange, Eds.), Lecture Notes in Artificial Intelligence 961, pp. 190 - 258, Springer-Verlag 1995.
    Abstract.
  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 Artificial Intelligence 3847, pp. 1 - 24, Springer 2006.
    Abstract.
  6. Yohji Akama and Thomas Zeugmann,
    Consistency Conditions for Inductive Inference of Recursive Functions,
    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. 251 - 264, Springer 2007.
    Abstract.
  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.
    Abstract.
  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.
    Abstract.
  9. Thomas Zeugmann,
    PAC Learning,
    in “Encyclopedia of Machine Learning,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 745 - 753, Springer 2010 (Invited contribution).
    PAC Learning, second edition;
    inEncyclopedia of Machine Learning and Data Mining,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 949 - 959, Springer US 2017 (Invited contribution).
  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).
    Stochastic Finite Learning, second edition;
    inEncyclopedia of Machine Learning and Data Mining,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 1187 - 1191, Springer US 2017 (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).
    VC Dimension, second edition;
    inEncyclopedia of Machine Learning and Data Mining,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 1323 - 1327, Springer US 2017 (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).
    Epsilon Nets, second edition;
    inEncyclopedia of Machine Learning and Data Mining,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 409 - 410, Springer US 2017 (Invited contribution).
  13. Thomas Zeugmann,
    Epsilon Covers,
    in “Encyclopedia of Machine Learning,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 326, Springer 2010 (Invited contribution).
    Epsilon Covers, second edition;
    Encyclopedia of Machine Learning and Data Mining,” (Claude Sammut and Geoffrey I. Webb, Eds.), pp. 408 - 409, Springer US 2017 (Invited contribution).

For further information, please contact
Thomas Zeugmann

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


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