Publications

Journal Papers


Conference Papers

  • Naoki Abe

    • Polynomial Learnability and Locality of Formal Grammars.
    Proceedings of the 26th Meeting of A.C.L., June 1988.
  • Naoki Abe

    • Feasible Learnability of Formal Grammars and the Theory of Natural Language Acquisition.
    Proceedings of COLING, August 1988.
  • Naoki Abe

    • Polynomial Learnability of Semilinear Sets.
    Proceedings of the 1989 Workshop on Computational Learning Theory, August 1989.
  • Naoki Abe and Manfred Warmuth

    • On the Computational Complexity of Approximating Probability Distributions by Probabilistic Automata,
    Proceedings of the 1990 Workshop on Computational Learning Theory, August 1990.
  • Naoki Abe

    • Learning Commutative Deterministic Finite State Automata in Polynomial Time.
    Proceedings of the First International Workshop on Algorithmic Learning Theory, October 1990.
  • Naoki Abe, Jun-ichi Takeuchi and Manfred Warmuth.

    • Polynomial Learnability of Probabilistic Concepts with respect to the Kullback-Leibler Divergence,
    Proceedings of the 1991 Workshop on Computational Learning Theory, August 1991.
  • Naoki Abe

    • Preservation of Predictability Under Polynomially Sparse Variations and Its Applications,
    Proceedings of the Second Workshop on Algorithmic Learning Theory, October 1991.
  • Naoki Abe

    • On the Sample Complexity of Various Learning Strategies in the Probabilistic PAC Learning Models,
    Proceedings of the Second International Workshop on Nonmonotonic and Inductive Logics, 1992.
  • Naoki Abe and Jun-ichi Takeuchi

    • The Lob-Pass Problem and an On-line Learning Model of Rational Choice,
    Proceedings of the Sixth Annual ACM Workshop on Computational Learning Theory, August 1993.
  • Atsuyoshi Nakamura and Naoki Abe

    • Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries,
    Proceedings of the Fourth International Workshop on Algorithmic Learning Theory, November 1993.
  • Naoki Abe and Hiroshi Mamitsuka.

    • A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars,
    Proceedings of The Eleventh International Conference on Machine Learning, August 1994.
  • Hiroshi Mamitsuka and Naoki Abe

    • Predicting Location and Structure of Beta-Sheet Regions Using Stochastic Tree Grammar learning,
    Proceedings of The Second Workshop on Intelligent Systems for Molecular Biology, August 1994.
  • Atsuyoshi Nakamura, Naoki Abe and Junichi Takeuchi

    • Efficient Distribution-free Population Learning of Simple Concepts,
    Proceedings of the Fifth International Workshop on Algorithmic Learning Theory, November 1994.
  • Naoki Abe

    • Towards Realistic Theories of Learning,
    Proceedings of the Fifth International Workshop on Algorithmic Learning Theory, November 1994.
  • Hiroshi Mamitsuka and Naoki Abe

    • Prediction of Beta-sheet structures With Stochastic Tree Grammars,
    Genome Workshop, 1994.
  • Atsuyoshi Nakamura and Naoki Abe

    • On-line Learning of Binary and n-ary Relations Over Multi-dimensional Clusters,
    Proceedings of The Eighth Annual Conference on Computational Learning Theory, July 1995.
  • Naoki Abe, Hang Li and Atsuyoshi Nakamura

    • On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms,
    Proceedings of The Twelfth International Conference on Machine Learning, July 1995.
  • Hang Li and Naoki Abe

    • Generalizing Case Frames Using a Thesaurus and the MDL Principle,
    Recent Advances in Natural Language Processing, September, 1995.
  • Hang Li and Naoki Abe

    • Clustering Words with the MDL Principle,
    Proceedings of COLING96, 1996.
  • Hang Li and Naoki Abe

    • Learning Dependencies Between Case Frames,
    Proceedings of COLING96, 1996.
  • Naoki Abe and Hang Li

    • Learning Word Association Norms Using Tree Cut Pair Models,
    Proceedings of The Thirteenth International Conference on Machine Learning, July 1996.
  • Naoki Abe and Hiroshi Mamitsuka

    • Query Learning Strategies Using Boosting and Bagging ,
    Proceedings of The Fifteenth International Conference on Machine Learning, July 1998.
  • Atsuyoshi Nakamura and Naoki Abe

    • Collaborative Filtering Using Weighted Majority Prediction Algorithms,
    Proceedings of The Fifteenth International Conference on Machine Learning, July 1998.
  • Hang Li and Naoki Abe,

    • Word Clustering and Disambiguation Based on Co-occurrence Data,
    36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, (COLING-ACL'98) , August 1998.
  • Marc Langheinrich, Atsuyoshi Nakamura, Naoki Abe, Tomonari Kamba, and Yoshiyuki Koseki

    • Unintrusive Customization Techniques for Web Advertising,
    Proceedings of The 8th International World Wide Web Conference , May 1999.
  • Naoki Abe and Atsuyoshi Nakamura

    • Learning to Optimally Schedule Internet Banner Advertisements,
    Proceedings of The Sixteenth International Conference on Machine Learning, July 1999.
  • Naoki Abe and Phil M. Long

    • Associative Reinforcement Learning Using Linear Probabilistic Concepts,
    Proceedings of The Sixteenth International Conference on Machine Learning, July 1999.
  • Naoki Abe and Tomonari Kamba

    • A Web Marketing System with Automatic Pricing,
    Proceedings of The 9th International World Wide Web Conference , May 2000.
  • Hiroshi Mamitsuka and Naoki Abe

    • Efficient Mining from Large Databases by Query Learning ,
    Proceedings of The Seventeenth International Conference on Machine Learning, June 2000.
  • Atsuyoshi Nakamura, Naoki Abe, Hiroshi Matoba and Katsuhiro Ochiai

    • Automatic recording agent for digital video server,
    Proceedings of The ACM Multimedia Conference 2000 , October 2000.
  • Keiko Udaka, Hiroshi Mamitsuka, Yukinobu Nakaseko and Naoki Abe

    • Prediction of MHC Class I Binding Peptides by A Query Learning Algorithm based on Hidden Markov Models
    Proceedings of ICBP 2001 (Journal of Biological Physics, Vol. 28, No. 2) , February 2002.
  • Edwin Pednault, Naoki Abe, Bianca Zadrozny, Haixun Wang, Wei Fan and Chid Apte

    • Sequential Cost Sensitive Decision Making with Reinforcement Learning ,
    Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 2002.
  • Naoki Abe, Edwin Pednault, Haixun Wang, Bianca Zadrozny, Wei Fan and Chid Apte

    • Empirical Comparison of Various Reinforcement Learning Strategies in Sequential Targeted Marketing,
    2002 IEEE International Conference on Data Mining, December 2002.
  • Bianca Zadrozny, John Langford and Naoki Abe

    • Cost-sensitive Learning by Cost Proportionate Example Weighting,
    2003 IEEE International Conference on Data Mining, November 2003.
  • Naoki Abe, Bianca Zadrozny and John Langford

    • An Iterative Method for Multi-class Cost-sensitive Learning,
    Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2004.
  • Naoki Abe, Naval Verma, Chid Apte and Robert Schroko

    • Cross Channel Optimized Marketing by Reinforcement Learning,
    Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2004.
  • Naoki Abe, Bianca Zadrozny and John Langford

    • Outlier Detection by Active Learning,
    Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2006.
  • Hisashi Kashima and Naoki Abe

    • A Parameterized Probabilistic Model of Network Evolution for Supervised Link Prediction,
    2006 IEEE International Conference on Data Mining, November 2006.
  • Andrew Arnold, Yan Liu and Naoki Abe

    • Temporal Causal Modeling with Graphical Granger Methods,
    Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2007.


Book Chapters

  • Hiroshi Mamitsuka and Naoki Abe

    • Efficient Data Mining by Active Learning,
    Progress in Discovery Science, Arikawa and Shinohara, eds., State-of-the-Art Survey, LNAI 2281, Springer-Verlag, 2001.
  • Jun'ichi Takeuchi, Naoki Abe and Shin'ichi Amari

    • The Lob-Pass Problem,
    Foundations of Real World Intelligence, Uesaka, Kanerva and Asho, eds., CSLI, 2001.
  • Naoki Abe, Edwin Pednault, Bianca Zadrozny, Haixun Wang, Wei Fan, and Chid Apte

    • Sequential Risk Management in E-Business by Reinforcement Learning
    Handbook of Integrated Risk Management for E-Business: Measuring, Modeling and Managing Risk , A. Labbi, eds., J.Ross Publishing, 2005.


Books and Volumes Edited

  • Naoki Abe, Roni Khardon and Thomas Zeugmann, Eds.

    • Algorithmic Learning Theory: 12th International Conference, ALT 2001,
    Lecture Notes in Computer Science, Vol. 2225, Springer-Verlag, 2001.
  • Naoki Abe and Roni Khardon, Eds.

    • Special Issue on Algorithmic Learning Theory,
    Theoretical Computer Science Vol. 313, No.2, Elsevier, 2004.


Articles

  • Naoki Abe, Scott Weinstein and Daniel Osherson

    • Recent Developments in Computational Learning Theory in the United States,
    Journal of Information Processing Society of Japan, Vol. 32, No. 3, March 1991. In Japanese.
  • Naoki Abe

    • Recent Developments in the Probabilistic PAC Learning Models,
    NEC Research & Development, Vol. 34, No. 1, January 1993.
  • Naoki Abe

    • New Developments in Computational Learning Theory,
    Journal of the IEICE, 77(12), December 1994. In Japanese.
  • Naoki Abe

    • Towards Realistic Theories of Learning,
    Applied Mathematics (Ouyou Suuri) 1995. In Japanese.
  • Naoki Abe and Atsuyoshi Nakamura

    • Introduction to Active Learning,
    Journal of Information Processing Society of Japan, Vol. 38, No. 7, July 1997. In Japanese.
  • Naoki Abe and Atsuyoshi Nakamura

    • Research on Active Learning in Computational Learning Theory
    Journal of Information Processing Society of Japan, Vol. 38, No. 7, July 1997. In Japanese.
Translated Article
  • Yoav Freund and Robert Schapire (translated to Japanese by Naoki Abe)

    • A Short Introduction to Boosting,
    Journal of Japan Society for Artificial Intelligence, Vol. 14, No. 5, 771-780, 1999.


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