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International Symposium on Mathematical Foundations of Computer Science

MFCS 1994: Mathematical Foundations of Computer Science 1994 pp 393–402Cite as

Combinatorial results on the complexity of teaching and learning

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 841))

Abstract

Some recent work in computational learning theory has focused on the complexity of teaching by examples. In this paper we study two combinatorial measures expressing the number of examples needed to teach any consistent learner, called teaching dimension and universal teaching dimension. We give a general lower bound on the teaching dimension that improves previous results, relate the teaching complexity measures to some learning complexity measures and combinatorial parameters, and compute bounds on the teaching dimension(s) of natural Boolean concept classes. We also observe an analogy between the teaching model and some problems in the fault detection research, and make use of some results achieved within that framework.

Supported by an Universidad Complutense de Madrid fellowship awarded by the A. Dŭbcek Center, Comenius University, Bratislava, and by the ALTEC (Algorithms for Future Technologies) project of the EC Cooperative Action IC1000.

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Authors and Affiliations

  1. Department of Computer Science, Comenius University, 84215, Bratislava, Slovakia

    Tibor Hegedüs

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  1. Tibor Hegedüs
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Igor Prívara Branislav Rovan Peter Ruzička

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© 1994 Springer-Verlag Berlin Heidelberg

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Hegedüs, T. (1994). Combinatorial results on the complexity of teaching and learning. In: Prívara, I., Rovan, B., Ruzička, P. (eds) Mathematical Foundations of Computer Science 1994. MFCS 1994. Lecture Notes in Computer Science, vol 841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58338-6_86

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  • DOI: https://doi.org/10.1007/3-540-58338-6_86

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58338-7

  • Online ISBN: 978-3-540-48663-3

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