Abstract
Inductive Logic Programming (ILP) is a Machine Learning research field that has been quite successful in knowledge discovery in relational domains. ILP systems use a set of pre-classified examples (positive and negative) and prior knowledge to learn a theory in which positive examples succeed and the negative examples fail. In this paper we present a novel ILP system called April, capable of exploring several parallel strategies in distributed and shared memory machines.
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Fonseca, N.A., Silva, F., Camacho, R. (2006). April – An Inductive Logic Programming System. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds) Logics in Artificial Intelligence. JELIA 2006. Lecture Notes in Computer Science(), vol 4160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11853886_42
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DOI: https://doi.org/10.1007/11853886_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39625-3
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