Learning Logical Definitions from Relations
作者： J.R. Quinlan
关键词: Inductive transfer 、 Learning classifier system 、 Instance-based learning 、 Sequence learning 、 Computer science 、 Computational learning theory 、 Inductive logic programming 、 Multi-task learning 、 Algorithmic learning theory 、 Artificial intelligence
摘要: This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas have proved effective in attribute-value learning systems, but extends them to first-order formalism. new has been applied successfully several tasks taken the machine literature.
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John R. Anderson, Paul I. Kline, A learning system and its psychological implications international joint conference on artificial intelligence. pp. 16- 21 ,(1979)
Geoffrey E. Hinton, Learning distributed representations of concepts. Clarendon Press/Oxford University Press. ,(1989)
Ivan Bratko, Igor Kononenko, Bojan Cestnik, ASSISTANT 86: a knowledge-elicitation tool for sophisticated users EWSL'87 Proceedings of the 2nd European Conference on European Working Session on Learning. pp. 31- 45 ,(1987)
Ehud Y. Shapiro, An algorithm that infers theories from facts international joint conference on artificial intelligence. pp. 446- 451 ,(1981)
Patrick Henry Winston, Artificial intelligence (2nd ed.) Addison-Wesley Longman Publishing Co., Inc.. ,(1984)
Donald Michie, Expert systems in the micro-electronic age Edinburgh University Press. ,(1979)
MICHEL MANAGO, Knowledge intensive induction international conference on machine learning. pp. 151- 155 ,(1989) , 10.1016/B978-1-55860-036-2.50045-X
Patrick H. Winston, Learning Structural Descriptions From Examples The Psychology of Computer Vision. ,(1970)
Ehud Yehuda Shapiro, Algorithmic Program Debugging ,(1983)
Ivan Bratko, Prolog Programming for Artificial Intelligence ,(1986)