Skip to main content
SpringerLink
Log in

A machine learning workbench in a DOOD framework

  • Learning Issues
  • Conference paper
  • First Online:
Database and Expert Systems Applications (DEXA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1308))

Included in the following conference series:

Abstract

In this paper we present a machine learning workbench, which we have developed by making use of deductive object-oriented database (DOOD) technology. It provides a comfortable environment for performing a large variety of machine learning tasks. By deriving full benefit of the available powerful logic and object-oriented programming language, we have implemented an easily extendable representative collection of machine learning algorithms. As realistic case study for the feasibility of the workbench we applied it to the automatic acquisition of linguistic knowledge within a natural language database interface.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aha, D. W., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 7 (1991) 37–66

    Google Scholar 

  2. Barja, M. L. et al.: An effective deductive object-oriented database through language integration. Proc. of the Intl. Conf. on Very Large Data Bases (1994) 463–474

    Google Scholar 

  3. Daelemans, W., van den Bosch, A.: Generalisation performance of backpropagation learning on a syllabification task. Drossaers, M., Nijholt, A. (eds): TWLT3: Connectionism and Natural Language Processing. Twente University Press, Enschede (1992) 27–37

    Google Scholar 

  4. Daelemans, W., van den Bosch, A., Weijters, T.: IGTree: Using trees for compression and classification in lazy learning algorithms. Artificial Intelligence Review (to appear)

    Google Scholar 

  5. Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39:11 (1996) 58–64

    Google Scholar 

  6. Muggleton, S. (ed): Inductive Logic Programming. Academic Press, London (1992)

    Google Scholar 

  7. Quinlan, J. R.: Induction of decision trees. Machine Learning 1 (1986) 81–206

    Google Scholar 

  8. Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, California (1993)

    Google Scholar 

  9. Quinlan, J. R., Cameron-Jones, R. M.: Induction of logic programs: FOIL and related systems. New Generation Computing 13 (1995) 287–312

    Google Scholar 

  10. Winiwarter, W.: The Integrated Deductive Approach to Natural Language Interfaces. Ph.D. thesis, University of Vienna (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Science, Kyoto University, 606-01, Sakyo, Kyoto, Japan

    Werner Winiwarter & Yahiko Kambayashi

Authors
  1. Werner Winiwarter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yahiko Kambayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Abdelkader Hameurlain A Min Tjoa

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Winiwarter, W., Kambayashi, Y. (1997). A machine learning workbench in a DOOD framework. In: Hameurlain, A., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1997. Lecture Notes in Computer Science, vol 1308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022054

Download citation

  • DOI: https://doi.org/10.1007/BFb0022054

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63478-2

  • Online ISBN: 978-3-540-69580-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation