Using Symbiotic Evolution to Improve the Predictive Accuracy for Inductive Logic Programming
Author:
Affiliation:
1. Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science
Publisher
Japanese Society for Artificial Intelligence
Subject
Artificial Intelligence,Software
Link
http://www.jstage.jst.go.jp/article/tjsai/17/4/17_4_431/_pdf
Reference19 articles.
1. [Anglano 98] Anglano,C. and Giordana,A. and Bello,G.L. and Saitta,L. : An Experimental Evaluation of Coevolutive Concept Learning, Proceedings of the 15th International Conference on Machine Learning, pp. 19-27 (1998)
2. [Augier 95] Augier,S. and Venturini,G. and Kodratoff,Y. : Learning first order logic rules with a genetic algorithm, Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining, pp. 21-26 (1995)
3. [Braud 01] Braud,A. and Vrain,C. : A Genetic Algorithm for Propositionalization, Proceedings of the 11th International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, No.2157, pp. 27-40 (2001)
4. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity
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