Nothing Special   »   [go: up one dir, main page]

Skip to main content

On the Effect of Caching in Recursive Theory Learning

  • Conference paper
Inductive Logic Programming (ILP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3194))

Included in the following conference series:

Abstract

This paper focuses on inductive learning of recursive logical theories from a set of examples. This is a complex task where the learning of one predicate definition should be interleaved with the learning of the other ones in order to discover predicate dependencies. To overcome this problem we propose a variant of the separate-and-conquer strategy based on parallel learning of different predicate definitions. In order to improve its efficiency, optimization techniques are investigated and adopted solutions are described. In particular, two caching strategies have been implemented and tested on document processing datasets. Experimental results are discussed and conclusions are drawn.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Apt, K.R.: Logic programming. In: van Leeuwen, J. (ed.) Handbook of Theoretical Computer Science, vol. B, pp. 493–574. Elsevier, Amsterdam (1990)

    Google Scholar 

  2. Blockeel, H., Demoen, B., Jansseens, G., Van de casteele, H., Van Laer, W.: Two Advanced Transformations for Improving the Efficiency of an ILP System. In: Cussens, J., Frisch, A. (eds.) Proceedings of the Work-in-Progress Track at the 10th International Conference on Inductive Logic Programming, pp. 43–59 (2000)

    Google Scholar 

  3. Blockeel, H., De Raedt, L., Jacobs, N., Demoen, B.: Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3(1), 59–93 (1999)

    Article  Google Scholar 

  4. Boström, H.: Induction of Recursive Transfer Rules. In: Cussens, J. (ed.) Proceedings of the Language Logic and Learning Workshop, pp. 52–62 (1999)

    Google Scholar 

  5. Buntine, W.: Generalised subsumption and its applications to induction and redundancy. Artificial Intelligence 36, 149–176 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  6. Costa, V.S., Srinivasan, A., Camacho, R.: A note on two simple trasformations for improving the efficiency of an ILP system. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, p. 225. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  7. Cussens, J.: Part-of-speech tagging using Progol. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS(LNAI), vol. 1297, Springer, Heidelberg (1997)

    Google Scholar 

  8. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning Journal 26(2/3), 99–146 (1997)

    Article  MATH  Google Scholar 

  9. De Raedt, L., Lavrac, N.: Multiple predicate learning in two Inductive Logic Programming settings. Journal on Pure and Applied Logic 4(2), 227–254 (1996)

    MATH  Google Scholar 

  10. Khardon, R.: Learning to take Actions. Machine Learning 35(1), 57–90 (1999)

    Article  MATH  Google Scholar 

  11. Malerba, D., Esposito, F., Lisi, F.A., Altamura, O.: Automated Discovery of Dependencies Between Logical Components in Document Image Understanding. In: Proceedings of the 6th International Conference on Document Analysis and Recognition, Seattle,WA, pp. 174–178 (2001)

    Google Scholar 

  12. Malerba, D.: Learning Recursive Theories in the Normal ILP Setting, Fundamenta Informaticae, vol. 57(1), pp. 39–77 (2003)

    Google Scholar 

  13. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  14. Muggleton, S., Bryant, C.H.: Theory completion using inverse entailment. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 130–146. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  15. Nedellec, C., Ad, H., Bergadano, F., Tausend, B.: Declarative bias in ILP. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, Frontiers in Artificial Intelligence and Applications, vol. 32, pp. 82–103. IOS Press, Amsterdam (1996)

    Google Scholar 

  16. Nienhuys-Cheng, S.-W., de Wolf, R.: The Subsumption theorem in inductive logic programming: Facts and fallacies. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 265–276. IOS Press, Amsterdam (1996)

    Google Scholar 

  17. Plotkin, G.D.: A note on inductive generalization. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence 5, pp. 153–163. Edinburgh University Press, Edinburgh (1970)

    Google Scholar 

  18. Plotkin, G.D.: A further note on inductive generalization. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence 6, pp. 101–124. Edinburgh University Press, Edinburgh (1971)

    Google Scholar 

  19. Struyf, J., Blockeel, H.: Query optimisation in Inductive Logic Programming by Reordering Literals. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 329–346. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Berardi, M., Varlaro, A., Malerba, D. (2004). On the Effect of Caching in Recursive Theory Learning. In: Camacho, R., King, R., Srinivasan, A. (eds) Inductive Logic Programming. ILP 2004. Lecture Notes in Computer Science(), vol 3194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30109-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30109-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22941-4

  • Online ISBN: 978-3-540-30109-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics