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Coverage-Based Semi-distance between Horn Clauses

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1904))

Abstract

In the present paper we use the approach of height functions to defining a semi-distance measure between Horn clauses. This appraoch is already discussed elsewhere in the framework of propositional and sim- ple first order languages (atoms). Hereafter we prove its applicability for Horn clauses. We use some basic results from lattice theory and introduce a family of language independent coverage-based height functions.Then we show how these results apply to Horn clauses. We also show an exam- ple of conceptual clustering of first order atoms, where the hypotheses are Horn clauses.

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References

  1. A. Hutchinson Metrics on terms and clauses. In M vanSomeren and G. Widmer, editors, Machine Learning: EC ML-97, volume 1224 of Lecture Notes in Artificial Intelligence, pages 138–145. Springer-Verlag, 1997.

    Google Scholar 

  2. Z. Markov. An algebraic approach to inductive learning. In Proceedings of 13th International FLAIRS Conference, page in print, Orlando, Florida, May 22–25, 2000. AAAI Press.

    Google Scholar 

  3. Z. Markov and I. Marinchev. Metric-based inductive learning using semantic height functions. In R. L. de Mantaras and E. Plaza, editors, Machine Learning: ECML 2000, volume 1810 of Lecture Notes in Artificial Intelligence, page in print. Springer, 2000.

    Google Scholar 

  4. S. Muggleton. Inductive logic programming. In S. Muggleton, editor, Inductive Logic Programming, pages 3–28. Academic Press, 1992.

    Google Scholar 

  5. S.-H. Nienhuys-Cheng. Distance between herbrand interpretations: a measure for approximations to a target concept. Technical Report EUR-FEW-CS-97-05, Erasmus University, 1997.

    Google Scholar 

  6. J. Ramon, M. Bruynooghe, and W. V. Laer. Distance measure between atoms. Technical Report CW 264, Katholieke Universiteit Leuven, 1998.

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  7. J. Ramon, M. Bruynooghe, and W. V. Laer. A framework for defining a distance between first-order logic objects. Technical Report CW 263, Katholieke Universiteit Leuven, 1998.

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© 2000 Springer-Verlag Berlin Heidelberg

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Markov, Z., Marinchev, I. (2000). Coverage-Based Semi-distance between Horn Clauses. In: Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2000. Lecture Notes in Computer Science, vol 1904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45331-8_32

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  • DOI: https://doi.org/10.1007/3-540-45331-8_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41044-7

  • Online ISBN: 978-3-540-45331-4

  • eBook Packages: Springer Book Archive

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