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Derived sets and inductive inference

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Algorithmic Learning Theory (AII 1994, ALT 1994)

Abstract

The paper deals with using topological concepts in studies of the Gold paradigm of inductive inference. They are — accumulation points, derived sets of order α (α — constructive ordinal) and compactness. Identifiability of a class U of total recursive functions with a bound α on the number of mindchanges implies \(U^{(\alpha + 1)} = \not 0\). This allows to construct counter-examples — recursively enumerable classes of functions showing the proper inclusion between identification types: EX αEX α+1.

The presence of an accumulation point in a class W determines whether or not all FIN strategies can be split into two families so that any finite team identifying W contains strategies from both families. A combinatorial idea, used to show the absence of such a splitting in the case when the derived set \(W^d = \not 0\), leads to new identification types (FIN(2: *), etc.) which may be irreducible to the team identification types (e. g. FIN(k: m)).

Supported by Grant No. 93-599 from Latvian Councile of Science

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References

  1. K. Kuratowski. Topology. Vol. 1. Academic Press New York and London, 1966.

    Google Scholar 

  2. E. M. Gold. Language identification in the limit. Information and control, 10:5, 1967, pp. 447–474.

    Google Scholar 

  3. K. Kuratowski, A. Mostowski. Set Theory. North-Holland Publishing Company, 1967.

    Google Scholar 

  4. H. Rogers. Theory of Recursive Functions and Effective Computability. McGraw-Hill, New York, 1967.

    Google Scholar 

  5. R. Engelking. General Topology. Polish Scientific Publishers, Warszawa, 1977.

    Google Scholar 

  6. C. Smith. The power of Pluralism for Automatic Program Synthesis. Assoc. Comput. Mach., 29, 1982, pp. 1144–1165.

    Google Scholar 

  7. J. Case, C. Smith. Comparison of identification criteria for machine inductive inference, Theoret. Comput. Sci., 25(2), 1983, pp. 193–220.

    Google Scholar 

  8. K. Apsītis, R. Freivalds, M. Kriķis, R. Simanovskis, J. Smotrovs. Unions of identifiable classes of total recursive functions. In K. Jantke, editor, Analogical and Inductive Inference, pages 99–107. Springer-Verlag, 1992. Lecture Notes in Artificial Intelligence, No. 642.

    Google Scholar 

  9. K. Apsītis. Topological Considerations in Composing Teams of Learning Machines. (To be published in the proceedings of International Workshop on Algorithmic Learning for Knowledge Processing (GOSLER project), November 1993, Dagstuhl Castle, Germany.)

    Google Scholar 

  10. R. Freivalds, C. Smith. On the Role of Procrastination in Machine Learning. Information and Computation, 107(2), 1993, pp. 237–271.

    Google Scholar 

  11. K. Apsītis, R. Freivalds, C. Smith. Choosing a Learning Team: a Topological Approach. (Manuscript accepted for the STOC conferece held in Montreal, May 23–25, 1994.)

    Google Scholar 

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Setsuo Arikawa Klaus P. Jantke

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© 1994 Springer-Verlag Berlin Heidelberg

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Apsītis, K. (1994). Derived sets and inductive inference. 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_51

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  • DOI: https://doi.org/10.1007/3-540-58520-6_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58520-6

  • Online ISBN: 978-3-540-49030-2

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