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Sharpening Occam’s Razor

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Computing and Combinatorics (COCOON 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2387))

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Abstract

We provide a new representation-independent formulation of Occam’s razor theorem, based on Kolmogorov complexity. This new formulation allows us to:

  • Obtain better sample complexity than both length-based [4] and VC-based [3] versions of Occam’s razor theorem, in many applications.

  • Achieve a sharper reverse of Occam’s razor theorem than that of [5]. Specifically, we weaken the assumptions made in [5] and extend the reverse to superpolynomial running times.

Supported in part by the NSERC Operating Grant OGP0046506, ITRC, and NSF-ITR Grant 0085801 at UCSB.

Partially supported by an NSERC International Fellowship and ITRC.

Partially supported by the European Community through NeuroCOLT ESPRIT Working Group Nr. 8556. Affiliated with CWI and the University of Amsterdam.

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References

  1. M. Anthony and N. Biggs, Computational Learning Theory, Cambridge University Press, 1992.

    Google Scholar 

  2. A. Blum, T. Jiang, M. Li, J. Tromp, M. Yannakakis, Linear approximation of shortest common superstrings. Journal ACM, 41:4 (1994), 630–647.

    Article  MATH  MathSciNet  Google Scholar 

  3. A. Blumer and A. Ehrenfeucht and D. Haussler and M. Warmuth, Learnability and the Vapnik-Chervonenkis Dimension. J. Assoc. Comput. Mach., 35(1989), 929–965.

    MathSciNet  Google Scholar 

  4. A. Blumer and A. Ehrenfeucht and D. Haussler and M. Warmuth, Occam’s Razor. Inform. Process. Lett., 24(1987), 377–380.

    Article  MATH  MathSciNet  Google Scholar 

  5. R. Board and L. Pitt, On the necessity of Occam Algorithms. 1990 STOC, pp. 54–63.

    Google Scholar 

  6. A. Ehrenfeucht, D. Haussler, M. Kearns, L. Valiant. A general lower bound on the number of examples needed for learning. Inform. Computation, 82(1989), 247–261.

    Article  MATH  MathSciNet  Google Scholar 

  7. D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework. Artificial Intelligence, 36:2(1988), 177–222.

    Article  MATH  MathSciNet  Google Scholar 

  8. D. Haussler, N. Littlestone, and, M. Warmuth. Predicting {0,1}-functions on randomly drawn points. Information and Computation, 115:2(1994), 248–292.

    Article  MATH  MathSciNet  Google Scholar 

  9. T. Jiang and M. Li, DNA sequencing and string learning, Math. Syst. Theory, 29(1996), 387–405.

    MathSciNet  MATH  Google Scholar 

  10. M. Li. Towards a DNA sequencing theory. 31st IEEE Symp. on Foundations of Comp. Sci., 125–134, 1990.

    Google Scholar 

  11. M. Li and P. Vitányi. An Introduction to Kolmogorov Complexity and Its Applications. 2nd Edition, Springer-Verlag, 1997.

    Google Scholar 

  12. L. G. Valiant. A Theory of the Learnable. Comm. ACM, 27(11), 1134–1142, 1984.

    Article  MATH  Google Scholar 

  13. M.K. Warmuth. Towards representation independence in PAC-learning. In AII-89, pp. 78–103, 1989.

    Google Scholar 

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Li, M., Tromp, J., Vitányi, P. (2002). Sharpening Occam’s Razor. In: Ibarra, O.H., Zhang, L. (eds) Computing and Combinatorics. COCOON 2002. Lecture Notes in Computer Science, vol 2387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45655-4_44

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  • DOI: https://doi.org/10.1007/3-540-45655-4_44

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  • Print ISBN: 978-3-540-43996-7

  • Online ISBN: 978-3-540-45655-1

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