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Inductive inference from positive data: from heuristic to characterizing methods

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Grammatical Interference: Learning Syntax from Sentences (ICGI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1147))

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Abstract

The practical work in the area of inductive inference of regular languages has to rely on incomplete positive data and thus to be content with approximative or heuristic solutions. We consider here the heuristic inference methods and present a formal approach — the generalized quotient — to them. Generalized quotients allows us to define clearly the heuristics used in these methods, and to examine from a firm basis relations between results produced by different heuristics. Although this approach gives us some tools to compare the results obtained via different heuristics, we are still not able to tell formally what is the concept we have derived. To overcome this problem we need characterizing inference methods, where the result of the inference process can always be given a formal description. A general framework based on monotonic representation mappings is constructed for building such methods.

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Authors and Affiliations

  1. Department of Computer Science, University of Turku, Lemminkäisenkatu 14 A, 20520, Turku, Finland

    Timo Knuutila

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  1. Timo Knuutila
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Laurent Miclet Colin de la Higuera

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© 1996 Springer-Verlag Berlin Heidelberg

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Knuutila, T. (1996). Inductive inference from positive data: from heuristic to characterizing methods. In: Miclet, L., de la Higuera, C. (eds) Grammatical Interference: Learning Syntax from Sentences. ICGI 1996. Lecture Notes in Computer Science, vol 1147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033340

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  • DOI: https://doi.org/10.1007/BFb0033340

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61778-5

  • Online ISBN: 978-3-540-70678-6

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