Abstract
The research reported in this paper describes Fossil, an ILP system that uses a search heuristic based on statistical correlation. This algorithm implements a new method for learning useful concepts in the presence of noise. In contrast to Foil's stopping criterion, which allows theories to grow in complexity as the size of the training sets increases, we propose a new stopping criterion that is independent of the number of training examples. Instead, Fossil's stopping criterion depends on a search heuristic that estimates the utility of literals on a uniform scale. In addition we outline how this feature can be used for top- down pruning and present some preliminary results.
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© 1994 Springer-Verlag Berlin Heidelberg
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Fürnkranz, J. (1994). FOSSIL: A robust relational learner. In: Bergadano, F., De Raedt, L. (eds) Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science, vol 784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_54
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