Skip to main content
SpringerLink
Log in

Empirical Comparison of Competing Query Learning Methods

  • Conference paper
  • First Online:
Book cover Discovey Science (DS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1532))

Included in the following conference series:

Abstract

Query learning is a form of machine learning in which the learner has control over the learning data it receives. In the context of discovery science, query learning may prove to be relevant in at least two ways. One is as a method of selective sampling, when a huge set of unlabeled data is available but a relatively small number of these data can be labeled, and a method that can selectively ask valuable queries is desired. The other is as a method of experimental design, where a query learning method is used to inform the experimenter what experiments are to be performed next

This research was supported in part by the Grant-in-Aid of the Ministry of Education, Science, Sports and Culture, Japan.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. N. Abe, H. Li, and A. Nakamura. On-line learning of binary lexical relations using two-dimensional weighted majority algorithms. In Proc. 12th Int’l. Conference on Machine Learning, July 1995.

    Google Scholar 

  2. N. Abe and H. Mamitsuka. Query learning strategies using boosting and bagging. In Proceedings of the Fifteenth International Conference on Machine Learning, 1998.

    Google Scholar 

  3. D. Angluin. Learning regular sets from queries and counterexamples. Inform. Comput., 75(2):87–106, November 1987.

    Google Scholar 

  4. A. Nakamura and N. Abe. On-line learning of binary and n-ary relations over multidimensional clusters. In Proc. 8th Annu. Workshop on Comput. Learning Theory, July 1995.

    Google Scholar 

  5. G. Paass and J. Kindermann. Bayesian query construction for neural network models. In Advances in nueral information processing systems 7, pages 443–450, 1995.

    Google Scholar 

  6. H. S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proc. 5th Annu. Workshop on Comput. Learning Theory, pages 287–294. ACM Press, New York, NY, 1992.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NEC C& C Media Research Laboratories, 4-1-1 Miyazaki, Miyamae-ku, Kawasaki, 216-8555, Japan

    Naoki Abe, Hiroshi Mamitsuka & Atsuyoshi Nakamura

  2. Department of Computational Intelligence and Systems Sciences, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, 226, Japan

    Naoki Abe

Authors
  1. Naoki Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroshi Mamitsuka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atsuyoshi Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Informatics, Kyushu University, Fukuoka, 812-8581, USA

    Setsuo Arikawa

  2. Institute of Scientific and Industrial Research Devision of Intelligent Systems Science, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan

    Hiroshi Motoda

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Abe, N., Mamitsuka, H., Nakamura, A. (1998). Empirical Comparison of Competing Query Learning Methods. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_34

Download citation

  • DOI: https://doi.org/10.1007/3-540-49292-5_34

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65390-5

  • Online ISBN: 978-3-540-49292-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation