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Background knowledge and declarative bias in inductive concept learning

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Analogical and Inductive Inference (AII 1992)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 642))

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Abstract

There are two main limitations of classical inductive learning algorithms: the limited capability of taking into account the available background knowledge and the use of limited knowledge representation formalisms based on propositional logic. The paper presents a method for using background knowledge effectively in learning both attribute and relational descriptions. The method, implemented in the system LINUS, uses propositional learners in a more expressive logic programming framework. This allows for learning of logic programs in the form of constrained deductive hierarchical database clauses. The paper discusses the language bias imposed by the method and shows how a more expressive language of determinate logic programs can be used within the same framework.

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Authors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, 61000, Ljubljana, Slovenia

    Nada Lavrač & Sašo Džeroski

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  1. Nada Lavrač
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  2. Sašo Džeroski
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Klaus P. Jantke

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© 1992 Springer-Verlag Berlin Heidelberg

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Lavrač, N., Džeroski, S. (1992). Background knowledge and declarative bias in inductive concept learning. In: Jantke, K.P. (eds) Analogical and Inductive Inference. AII 1992. Lecture Notes in Computer Science, vol 642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56004-1_4

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  • DOI: https://doi.org/10.1007/3-540-56004-1_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56004-3

  • Online ISBN: 978-3-540-47339-8

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