Skip to main content

Generalized Graph Colorability and Compressibility of Boolean Formulae

  • Conference paper
  • First Online:
Algorithms and Computation (ISAAC 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1533))

Included in the following conference series:

Abstract

In this paper, we study the possibility of Occam’s razors for a widely studied class of Boolean Formulae: Disjunctive Normal Forms (DNF). An Occam’s razor is an algorithm which compresses the knowledge of observations (examples) in small formulae. We prove that approximating the minimally consistent DNF formula, and a generalization of graph colorability, is very hard. Our proof technique is such that the stronger the complexity hypothesis used, the larger the inapproximability ratio obtained. Our ratio is among the first to integrate the three parameters of Occam’s razors: the number of examples, the number of description attributes and the size of the target formula labelling the examples. Theoretically speaking, our result rules out the existence of efficient deterministic Occam’s razor algorithms for DNF. Practically speaking, it puts a large worst-case lower bound on the formulae’s sizes found by learning systems proceeding by rule searching.

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

Access this chapter

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.

References

  1. H. Aizenstein and L. Pitt. Exact learning of read-k-disjoint DNF and not-so-disjoint-DNF. In Proc. of the 5th International Conference on Computational Learning Theory, pages 71–76, 1992.

    Google Scholar 

  2. H. Aizenstein and L. Pitt. On the learnability of Disjunctive Normal Form formulas. Machine Learning, 19:183–208, 1995.

    MATH  Google Scholar 

  3. J. L. Balcazar, J. Diaz, and J. Gabarro. Structural Complexity I. Springer Verlag, 1988.

    Google Scholar 

  4. U. Berggren. Linear time deterministic learning of k-term-DNF. In Proc. of the 6th International Conference on Computational Learning Theory, pages 37–40, 1993.

    Google Scholar 

  5. A. Blum, R. Khardon, E. Kushilevitz, L. Pitt, and D. Roth. On learning read-k-satisfy-j DNF. In Proc. of the 7International Conference on Computational Learning Theory, pages 110–117, 1994.

    Google Scholar 

  6. A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Occam’s razor. Information Processing Letters, pages 377–380, 1987.

    Google Scholar 

  7. C. Brunk and M. Pazzani. Noise-tolerant relational concept learning. In Proc. of the 8th International Conference on Machine Learning, 1991.

    Google Scholar 

  8. N. H. Bshouty, Z. Chen, S. E. Decatur, and S. Homer. On the learnability of zn-DNF formulas. In Proc. of the 8th International Conference on Computational Learning Theory, pages 198–205, 1995.

    Google Scholar 

  9. W. W. Cohen. PAC-learning a restricted class of recursive logic programs. In Proc. of AAAI-93, pages 86–92, 1993.

    Google Scholar 

  10. W. W. Cohen. Fast effective rule induction. In Proc. of the 12th International Conference on Machine Learning, pages 115–123, 1995.

    Google Scholar 

  11. C. de la Higuera. Characteristic sets for polynomial grammatical inference. Machine Learning, pages 1–14, 1997.

    Google Scholar 

  12. L. de Raedt. Iterative concept learning and construction by analogy. Machine Learning, pages 107–150, 1992.

    Google Scholar 

  13. U. Feige and J. Kilian. Zero knowledge and the chromatic number. draft, 1996.

    Google Scholar 

  14. M.R. Garey and D.S. Johnson. Computers and Intractability, a guide to the theory of NP-Completeness. Bell Telephone Laboratories, 1979.

    Google Scholar 

  15. S. A. Goldman and H. D. Mathias. Learning k-term-DNF formulas with an incomplete membership oracle. In Proc. of the 5th International Conference on Computational Learning Theory, pages 77–84, 1992.

    Google Scholar 

  16. J. Hastad. Clique is hard to approximate within n1-ε. In FOCS’96, pages 627–636, 1996.

    Google Scholar 

  17. R.C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning, pages 63–91, 1993.

    Google Scholar 

  18. M. J. Kearns and U. V. Vazirani. An Introduction to Computational Learning Theory. M.I.T. Press, 1994.

    Google Scholar 

  19. M.J. Kearns, M. Li, L. Pitt, and L. Valiant. On the learnability of boolean formulae. Proceedings of the Nineteenth Annual A.C.M. Symposium on Theory of Computing, pages 285–295, 1987.

    Google Scholar 

  20. R. Khardon. On using the fourier transform to learn disjoint DNF. Information Processing Letters, pages 219–222, 1994.

    Google Scholar 

  21. N. Lavrac, S. Dzeroski, and M. Grobelnik. Learning non-recursive definitions of relations with linus. In European Working Session in Learning, 1991.

    Google Scholar 

  22. K. Lund and M. Yannakakis. On the hardness of approximating minimization problems. In Proc. of the 25th Symposium on the Theory of Computing, pages 286–293, 1993.

    Google Scholar 

  23. Y. Mansour. An O(nlog log n) algorithm for dnf under the uniform distribution. In Proc. of the 5th International Conference on Computational Learning Theory, pages 53–61, 1992.

    Google Scholar 

  24. S. Muggleton and C. Feng. Efficient induction of logic programs. In Inductive Logic Programming, 1994.

    Google Scholar 

  25. R. Nock and O. Gascuel. On learning decision committees. In Proc. of the 12th International Conference on Machine Learning, pages 413–420, 1995.

    Google Scholar 

  26. J. Pagallo and D. Haussler. Boolean feature discovery in empirical learning. Machine Learning, 1990.

    Google Scholar 

  27. K. Pillaipakkamnatt and V. Raghavan. On the limits of proper learnability of subclasses of DNF formulae. In Proc. of the 7th International Conference on Computational Learning Theory, pages 118–129, 1994.

    Google Scholar 

  28. L. Pitt and L. G. Valiant. Computational limitations on learning from examples. J. ACM, pages 965–984, 1988.

    Google Scholar 

  29. J. R. Quinlan. Learning logical definition from relations. Machine Learning, pages 239–270, 1990.

    Google Scholar 

  30. J. R. Quinlan. C4.5: programs for machine learning. Morgan Kaufmann, 1994.

    Google Scholar 

  31. J. R. Quinlan. MDL and categorical theories (continued). In Proc. of the 12th International Conference on Machine Learning, pages 464–470, 1995.

    Google Scholar 

  32. C. Rouveirol. ITOU: induction of first-order theories. Inductive Logic Programming, 1992.

    Google Scholar 

  33. S. B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S. E. Fahlman, D. Fisher, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R. S. Michalski, T. Mitchell, P. Pachowicz, Y. Reich, H. Vafaie, W. Van de Welde, W. Wenzel, J. Wnek, and J. Zhang. The MONK’s problems: a performance comparison of different lear ning algorithms. Technical Report CMU-CS-91-197, Carnegie Mellon University, 1991.

    Google Scholar 

  34. L. G. Valiant. A theory of the learnable. Communications of the ACM, pages 1134–1142, 1984.

    Google Scholar 

  35. L. G. Valiant. Learning disjunctions of conjunctions. In Proc. of the 9th IJCAI, pages 560–566, 1985.

    Google Scholar 

  36. J Wnek and R. Michalski. Hypothesis-driven constructive induction in AQ17. In Proc. of the 12th IJCAI, 1991.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nock, R., Jappy, P., Sallantin, J. (1998). Generalized Graph Colorability and Compressibility of Boolean Formulae. In: Chwa, KY., Ibarra, O.H. (eds) Algorithms and Computation. ISAAC 1998. Lecture Notes in Computer Science, vol 1533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49381-6_26

Download citation

  • DOI: https://doi.org/10.1007/3-540-49381-6_26

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65385-1

  • Online ISBN: 978-3-540-49381-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics