Skip to main content
SpringerLink
Log in

LPMEME: A statistical method for inductive logic programming

  • Learning I: Induction
  • Conference paper
  • First Online:
Book cover Advances in Artifical Intelligence (Canadian AI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1081))

Abstract

This paper describes LPMEME, a new learning algorithm for inductive logic programming that uses statistical techniques to find first-order patterns. LPMEME takes as input examples in the form of logical facts and outputs a first-order theory that is represented to some degree in all of the examples. LPMEME uses an underlying statistical model whose parameters are learned using expectation maximization, an iterative gradient descent method for maximum likelihood parameter estimation. The underlying statistical model is described and the EM algorithm developed. Experimental tests show that LPMEME can learn first-order concepts and can be used to find approximate solutions to the subgraph isomorphism problem.

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. John Allen and Kevin Thompson. Probabilistic concept formation in relational domains. In Proceedings of the Eighth International Workshop on Machine Learning, pages 375–379, 1991.

    Google Scholar 

  2. Peter Artymiuk, Helen Gridley, Andrew Poirrette, David Rice, Elizabeth Ujah, and Peter Willett. Identification of β-sheet motifs, of ψ-loops, and of patterns of amino acid residues in three-dimensional protein structures using a subgraph-isomorphism algorithm. Journal of Chemical Information and Computer Sciences, 34(1):54–62, Jan-Feb 1994.

    Google Scholar 

  3. Timothy L. Bailey and Charles Elkan. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB'94), pages 28–36, Aug 1994.

    Google Scholar 

  4. Clifford Brunk and Michael Pazzani. An investigation of noise-tolerant relational concept learning algorithms. In Proceedings of the Eighth International Workshop on Machine Learning, pages 389–393, 1991.

    Google Scholar 

  5. A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 36:1–21, 1976.

    Google Scholar 

  6. Richard Duda and Peter Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, Inc., 1973.

    Google Scholar 

  7. Michael Garey and David Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman and Company, 1979.

    Google Scholar 

  8. Steven Muggleton. Inductive logic programming. New Generation Computing, 8:21–33, 1991.

    Google Scholar 

  9. Steven Muggleton. Inductive logic programming: Derivations, successes and shortcomings. In Proceedings of the Tenth International Conference on Machine Learning, pages 21–33, 1993.

    Google Scholar 

  10. J.R. Quinlan. Induction of decision trees. Machine Learning, 1(1):81–106, 1986.

    Google Scholar 

  11. J.R. Quinlan. Learning logical definitions from relations. Machine Learning, 5(3):239–66, 1990.

    Google Scholar 

  12. J.R. Quinlan. FOIL: A midterm report. In Proceedings of the Tenth International Conference on Machine Learning, pages 3–19, 1993.

    Google Scholar 

  13. Michael Sternberg, Ross King, Richard Lewis, and Steven Muggleton. Application of machine learning to structural molecular biology. Phil. Trans. R. Soc. Land. B, 344:365–71, 1994.

    Google Scholar 

  14. Wen-Hsiang Tsai and King-Sun Fu. Subgraph error-correcting isomorphisms for syntactic pattern recognition. IEEE Trans. on Systems, Man, and Cybernetics, 13(1):48–62, Jan-Feb 1983.

    Google Scholar 

  15. Jason Wang, Gung-Wei Chirn, Thomas Marr, Bruce Shapiro, Dennis Shasha, and Kaizhong Zhang. Combinatorial pattern discovery for scientific data: Some preliminary results. In 1994 ACM SIGMOD International Conference on Management of Data, pages 115–125, 1994.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering 0114, University of California, San Diego, 92093, La Jolla, CA

    Karan Bhatia & Charles Elkan

Authors
  1. Karan Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charles Elkan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Gordon McCalla

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bhatia, K., Elkan, C. (1996). LPMEME: A statistical method for inductive logic programming. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_54

Download citation

  • DOI: https://doi.org/10.1007/3-540-61291-2_54

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61291-9

  • Online ISBN: 978-3-540-68450-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation