Abstract
The goals of this presentation are as follows: - Review some key ideas and developments in inductive logic programming. - Show how these ideas can be used in other learning settings, and in particular for the computational scientific discovery of quantitative laws. - Encourage more research on learning in rich representations, such as relational representations and differential equations, which can be used for modeling a variety of real world problems.
This paper also appears in the Proceedings of the Nineteenth Conference on Machine Learning (ICML- 2002), published by Morgan Kaufmann.
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Džeroski, S. (2003). Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_23
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DOI: https://doi.org/10.1007/3-540-36468-4_23
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