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Learning in Rich Representations: Inductive Logic Programming and Computational
Scientific Discovery
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Saso Dzeroski |
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Joint invited talk at ICML-02
and ILP-02, Sydney,
Australia |
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The goals of the talk were to:
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Review some key ideas and developments in inductive logic programming
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Show how these ideas can be used in other learning settings, and in particular
for the computational scientific discovery of quantitative laws.
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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.
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Dzeroski, S. (2002) Learning in Rich Representations:
Inductive Logic Programming and Computational Scientific Discovery. In
Proceedings
of the Nineteenth International Conference on Machine Learning,
pages
701-702. Morgan Kaufmann, San Francisco, CA.
Abstract: [ps] [pdf]
Slides: [ps] [pdf]
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