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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Keywords
- Association Rule
- Inductive Logic
- Inductive Logic Programming
- Average Execution Time
- Machine Learn Research
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Costa, V.S., Srinivasan, A., Camacho, R., Blockeel, H., Demoen, B., Janssens, G., Struyf, J., Vandecasteele, H., Van Laer, W.: Query transformations for improving the efficiency of ilp systems. Journal of Machine Learning Research 4, 465–491 (2003)
Železný, F., Srinivasan, A., Page, D.L.: Lattice-search runtime distributions may be heavy-tailed. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 333–345. Springer, Heidelberg (2003)
Cussens, J.: Part-of-speech disambiguation using ilp. Technical Report PRG-TR-25-96, Oxford University Computing Laboratory (1996)
Camacho, R.: As lazy as it can be. In: The Eighth Scandinavian Conference on Artificial Intelligence (SCAI 2003), Bergen, Norway, pp. 47–58 (November 2003)
Rocha, R., Fonseca, N.A., Santos Costa, V.: On Applying Tabling to Inductive Logic Programming. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 707–714. Springer, Heidelberg (2005)
Fonseca, N.A., Silva, F., Santos Costa, V., Camacho, R.: A pipelined data-parallel algorithm for ILP. In: Proceedings of 2005 IEEE International Conference on Cluster Computing. IEEE, Los Alamitos (2005)
Fonseca, N.A., Silva, F., Camacho, R.: Strategies to parallelize ILP systems. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 136–153. Springer, Heidelberg (2005)
Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4), 245–286 (1995)
Dehaspe, L., Toironen, H.: Relational Data Mining. In: Discovery of relational association rules, pp. 189–208. Springer, Heidelberg (2000)
Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)
Davies, T., Russell, S.: A logical approach to reasoning by analogy. In: Proceedings of the 10th International Joint Conference on Artificial Intelligence, Los Altos, California, pp. 264–270 (1987)
Srinivasan, A.: The Aleph Manual (2003)
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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
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