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Training digraphs

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Algorithmic Learning Theory (AII 1994, ALT 1994)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 872))

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Abstract

A formal definition of what it means for a machine to learn a collection of concepts in an order determined by a finite acyclic digraph of recursive functions is presented. We show that given a labelled graph G=(V, E) representing the learning structure, there are sets S such that in order to learn a program corresponding to some node i, a machine must have precisely learned programs corresponding to all the predecessor nodes.

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References

  1. D. Angluin, W.I. Gasarch, C.H. Smith, Training Sequences, Theoretical Computer Science 66 (1989) pp. 255–272.

    Google Scholar 

  2. D. Angluin, C.H. Smith, Inductive inference: theory and methods, Computing Survey 15 (1983) pp. 237–269.

    Google Scholar 

  3. J. Case and C. Smith, Comparison of identification criteria for machine inductive inference, Theoretical Computer Science 25 (1983) pp. 193–220.

    Google Scholar 

  4. N.J. Cutland, Computability: An introduction to recursive function theory, Cambridge University Press (1980).

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  5. R.P. Daley and C.H. Smith, On the complexity of inductive inference, Information and Control 69 (1986) pp. 12–40.

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  6. M.D. Davis, E.J. Weyuker, Computability, Complexity, and Languages, Academic Press (1983).

    Google Scholar 

  7. E.M. Gold, Language identification in the limit, Information and Control 10 (1967) pp. 447–474.

    Google Scholar 

  8. D.B. Lenat, E.A. Feigenbaum: On the thresholds of knowledge, Artificial Intelligence 47 (1991) pp. 185–250.

    Google Scholar 

  9. C.H. Papadimitriou, K. Steiglitz Combinatorial Optimization: Algorithms and Complexity, Prentice Hall (1982).

    Google Scholar 

  10. H. Rogers, Jr., Theory of Recursive Functions and Effective Computability, The MIT Press (1988).

    Google Scholar 

  11. Robert I. Soare, Recursively Enumerable Sets and Degrees, Springer-Verlag (1980).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Maryland, 20742, College Park, MD

    Hsieh-Chang Tu & Carl H. Smith

Authors
  1. Hsieh-Chang Tu
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  2. Carl H. Smith
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Editor information

Setsuo Arikawa Klaus P. Jantke

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© 1994 Springer-Verlag Berlin Heidelberg

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Tu, HC., Smith, C.H. (1994). Training digraphs. In: Arikawa, S., Jantke, K.P. (eds) Algorithmic Learning Theory. AII ALT 1994 1994. Lecture Notes in Computer Science, vol 872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58520-6_63

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  • DOI: https://doi.org/10.1007/3-540-58520-6_63

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58520-6

  • Online ISBN: 978-3-540-49030-2

  • eBook Packages: Springer Book Archive

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