Abstract
The principles of instance based function learning are presented. In IBFL one is given a set of positive examples of a functional predicate. These examples are true ground facts that illustrate the input output behaviour of the predicate. The purpose is then to predict the output of the predicate given a new input. Further assumptions are that there is no background theory and that the inputs and outputs of the predicate consist of structured terms. IBFL is a novel technique that addresses this problem and that combines ideas from instance based learning, first order distances and analogical or case based reasoning. We also argue that IBFL is especially useful when there is a need for handling complex and deeply nested terms. Though we present the technique in isolation, it might be more useful as a component of a larger system to deal e.g. with the logic, language and learning challenge.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
D. W. Aha, D. Kibler, and M. K. Albert. Instance-based learning algorithms. Machine Learning, 6(1):37–66, January 1991.
W. Emde and D. Wettschereck. Relational instance-based learning. In L. Saitta, editor, Proceedings of the 13th International Conference on Machine Learning, pages 122–130. Morgan Kaufmann, 1996.
Thomas G. Evans. A program for the solution of a class of geometric-analogy intelligence-test questions. In Marvin L. Minsky, editor, Semantic Information Processing, pages 271–353. MIT Press, Cambridge, Massachusetts, 1968.
P.A. Flach. Strongly typed inductive concept learning. In D. Page, editor, Proceedings of the 8th International Conference on Inductive Logic Programming, volume 1446, pages 185–194. Springer-Verlag, 1998.
E. Hirowatari and S. Arikawa. Explanation based generalisation by analogical reasoning. In Proceedings of the 2nd International Workshop on Inductive Logic Programming. Institute for New Generation Computer Technology, 1992.
G. Huet. Conuent reductions: Abstract properties and applications to term rewriting systems. Journal of the Association for Computing Machinery, 27(4):797–821, 1980.
A. Hutchinson. Metrics on terms and clauses. In Proceedings of the 9th European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, pages 138–145. Springer-Verlag, 1997.
D. Kazakov, S. Pulman, and S. Muggleton. The fracas dataset and the lll challenge. Technical report, 1998.
N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.
S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20:629–679, 1994.
Shan-Hwei Nienhuys-Cheng. Distance between herbrand interpretations: A measure for approximations to a target concept. In Proceedings of the 7th International Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence. Springer-Verlag, 1997.
G. Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5, pages 153–163. Edinburgh University Press, 1970.
J. Ramon and M. Bruynooghe. A framework for defining distances between first-order logic objects. In Proceedings of the 8th International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, pages 271–280. Springer-Verlag, 1998.
J. Ramon, M. Bruynooghe, and W. Van Laer. Distance measures between atoms. In Proceedings of the CompulogNet Area Meeting on ‘Computational Logic and Machine Learning”, pages 35–41, 1998.
K. Sadohara and M. Haraguchi. Analogical logic program synthesis from examples. In N. Lavrač and S. Wrobel, editors, Proceedings of the 8th European Conference on Machine Learning, volume 912 of Lecture Notes in Artificial Intelligence, pages 232–244, Berlin, Heidelberg, New York, 1995. Springer-Verlag.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ramon, J., De Raedt, L. (1999). Instance based function learning. In: Džeroski, S., Flach, P. (eds) Inductive Logic Programming. ILP 1999. Lecture Notes in Computer Science(), vol 1634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48751-4_25
Download citation
DOI: https://doi.org/10.1007/3-540-48751-4_25
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66109-2
Online ISBN: 978-3-540-48751-7
eBook Packages: Springer Book Archive