Skip to main content
SpringerLink
Log in

On identification by teams and probabilistic machines

  • 1 Inductive Inference Theory
  • Chapter
  • First Online:
Algorithmic Learning for Knowledge-Based Systems

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 961))

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. L. Blum and M. Blum. Toward a mathematical theory of inductive inference. Information and Control, 28:125–155, 1975.

    Google Scholar 

  2. M. Blum. A machine independent theory of the complexity of recursive functions. Journal of the ACM, 14:322–336, 1967.

    Google Scholar 

  3. J. Case. Periodicity in generations of automata. Mathematical Systems Theory, 8:15–32, 1974.

    Google Scholar 

  4. J. Case and C. Lynes. Machine inductive inference and language identification. In M. Nielsen and E. M. Schmidt, editors, Proceedings of the 9th International Colloquium on Automata, Languages and Programming, volume 140, pages 107–115. Springer-Verlag, Berlin, 1982.

    Google Scholar 

  5. J. Case and C. Smith. Comparison of identification criteria for machine inductive inference. Theoretical Computer Science, 25:193–220, 1983.

    Google Scholar 

  6. R. Daley. On the error correcting power of pluralism in BC-type inductive inference. Theoretical Computer Science, 24:95–104, 1983.

    Google Scholar 

  7. R. P. Daley. Inductive inference hierarchies: Probabilistic vs pluralistic. Lecture Notes in Computer Science, 215:73–82, 1986.

    Google Scholar 

  8. R. P. Daley and B. Kalyanasundaram. Capabilities of probabilistic learners with bounded mind changes. In Proceedings of the Sixth Annual Conference on Computational Learning Theory, Santa Cruz, California, pages 182–191. A. C. M. Press, 1993.

    Google Scholar 

  9. R. P. Daley and B. Kalyanasundaram. Use of reduction arguments in determining popperian fin-type learning capabilities. In K.P. Jantke, S. Kobayashi, E. Tomita, and T. Yokomori, editors, Proceedings of the Fourth International Workshop on Algorithmi Learning Theory, Tokyo, Japan, Lecture Notes in Artificial Intelligence, No. 744, pages 173–186. Springer-Verlag, November 1993.

    Google Scholar 

  10. R. P. Daley, B. Kalyanasundaram, and M. Velauthapillai. Breaking the probability 1/2 barrier in fin-type learning. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, pages 203–217. A. C. M. Press, 1992.

    Google Scholar 

  11. R. P. Daley, B. Kalyanasundaram, and M. Velauthapillai. The power of probabilism in popperian finite learning. In Proceedings of the Third International Workshop on Analogical and Inductive Inference, Dagstuhl Castle, Germany, pages 151–169, October 1992.

    Google Scholar 

  12. R. P. Daley, B. Kalyanasundaram, and M. Velauthapillai. Capabilities of fallible finite learning. In Proceedings of the Sixth Annual Conference on Computational Learning Theory, Santa Cruz, California, pages 199–208. A. C. M. Press, 1993.

    Google Scholar 

  13. R. P. Daley, L. Pitt, M. Velauthapillai, and T. Will. Relations between probabilistic and team one-shot learners. In L. Valiant and M. Warmuth, editors, Proceedings of the Workshop on Computational Learning Theory, pages 228–239. Morgan Kaufmann Publishers, Inc., 1991.

    Google Scholar 

  14. R.P. Daley. Transformation of probabilistic learning strategies into deterministic learning strategies. In D. Haussler and L. Pitt, editors, Proceedings of the Workshop on Computational Learning Theory, pages 157–163. Morgan Kaufmann Publishers, Inc., 1988.

    Google Scholar 

  15. R. Freivalds. Functions computable in the limit by probabilistic machines. Mathematical Foundations of Computer Science, 1975.

    Google Scholar 

  16. R. Freivalds. Finite identification of general recursive functions by probabilistic strategies. In Proceedings of the Conference on Algebraic, Arithmetic and Categorical Methods in Computation Theory, pages 138–145. Akedemie-Verlag, Berlin, 1979.

    Google Scholar 

  17. R Freivalds, C. H. Smith, and M. Velauthapillai. Trade-off among parameters affecting inductive inference. Information and Computation, 82:323–349, 1989.

    Google Scholar 

  18. E. M. Gold. Language identification in the limit. Information and Control, 10:447–474, 1967.

    Google Scholar 

  19. J. Hopcroft and J. Ullman. Introduction to Automata Theory Languages and Computation. Addison-Wesley Publishing Company, 1979.

    Google Scholar 

  20. S. Jain and A. Sharma. Finite learning by a team. In M. Fulk and J. Case, editors, Proceedings of the Third Annual Workshop on Computational Learning Theory, Rochester, New York, pages 163–177. Morgan Kaufmann Publishers, Inc., August 1990.

    Google Scholar 

  21. S. Jain and A. Sharma. Language learning by a team. In M. S. Paterson, editor, Proceedings of the 17th International Colloquium on Automata, Languages and Programming, pages 153–166. Springer-Verlag, July 1990.

    Google Scholar 

  22. S. Jain and A. Sharma. Computational limits on team identification of languages. Technical Report 9301, School of Computer Science and Engineering; University of New South Wales, 1993.

    Google Scholar 

  23. S. Jain and A. Sharma. On aggregating teams of learning machines. In K.P. Jantke, S. Kobayashi, E. Tomita, and T. Yokomori, editors, Proceedings of the Fourth International Workshop on Algorithmi Learning Theory, Tokyo, Japan, Lecture Notes in Artificial Intelligence, No. 744, pages 150–163. Springer-Verlag, November 1993.

    Google Scholar 

  24. S. Jain and A. Sharma. Probability is more powerful than team for language identification. In Proceedings of the Sixth Annual Conference on Computational Learning Theory, Santa Cruz, California, pages 192–198. ACM Press, July 1993.

    Google Scholar 

  25. S. Jain and A. Sharma. On aggregating teams of learning machines. Technical Report 9405, School of Computer Science and Engineering; University of New South Wales, 1994.

    Google Scholar 

  26. S. Jain, A. Sharma, and M. Velauthapillai. Finite identification of function by teams with success ration 1/2 and above. In preparation, 1994.

    Google Scholar 

  27. M. Machtey and P. Young. An Introduction to the General Theory of Algorithms. North Holland, New York, 1978.

    Google Scholar 

  28. D. Osherson, M. Stob, and S. Weinstein. Aggregating inductive expertise. Information and Control, 70:69–95, 1986.

    Google Scholar 

  29. D. Osherson, M. Stob, and S. Weinstein. Systems that Learn, An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge, Mass., 1986.

    Google Scholar 

  30. D. Osherson and S. Weinstein. Criteria of language learning. Information and Control, 52:123–138, 1982.

    Google Scholar 

  31. L. Pitt. A characterization of probabilistic inference. PhD thesis, Yale University, 1984.

    Google Scholar 

  32. L. Pitt. Probabilistic inductive inference. Journal of the ACM, 36:383–433, 1989.

    Google Scholar 

  33. L. Pitt and C. Smith. Probability and plurality for aggregations of learning machines. Information and Computation, 77:77–92, 1988.

    Google Scholar 

  34. H. Rogers. Gödel numberings of partial recursive functions. Journal of Symbolic Logic, 23:331–341, 1958.

    Google Scholar 

  35. H. Rogers. Theory of Recursive Functions and Effective Computability. McGraw Hill, New York, 1967. Reprinted, MIT Press 1987.

    Google Scholar 

  36. C. Smith. The power of pluralism for automatic program synthesis. Journal of the ACM, 29:1144–1165, 1982.

    Google Scholar 

  37. M. Velauthapillai. Inductive inference with bounded number of mind changes. In Proceedings of the Workshop on Computational Learning Theory, pages 200–213, 1989.

    Google Scholar 

  38. R. Wiehagen, R. Freivalds, and E. B. Kinber. On the power of probabilistic strategies in inductive inference. Theoretical Computer Science, 28:111–133, 1984.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Systems Science, National University of Singapore, 0511, Singapore, Republic of Singapore

    Sanjay Jain

  2. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, NSW, Australia

    Arun Sharma

Authors
  1. Sanjay Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arun Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Klaus P. Jantke Steffen Lange

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Jain, S., Sharma, A. (1995). On identification by teams and probabilistic machines. In: Jantke, K.P., Lange, S. (eds) Algorithmic Learning for Knowledge-Based Systems. Lecture Notes in Computer Science, vol 961. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60217-8_7

Download citation

  • DOI: https://doi.org/10.1007/3-540-60217-8_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60217-0

  • Online ISBN: 978-3-540-44737-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation