Abstract
Given a set of noisy training examples, an approximate theory probably including multiple predicates and background knowledge, to acquire a more accurate theory is a more realistic problem in knowledge acquisition with machine learning methods. An algorithm called KNOWAR[2] that combines a multiple predicate learning module and a theory revision module has been used to deal with such a problem, where inductive logic programming methods are employed
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References
B. Dolšak, I. Bratko, and A. Jezernik. Finite element mesh design: An engineering domain for ILP application. In Proc. of the Fourth International Workshop on Inductive Logic Programming, pages 305–320, Germany, 1994.
Xiaolong Zhang. Knowledge Acquisition and Revision with First Order Logic Induction. PhD thesis, Dept. of Computer Science, Tokyo Institute of Technology, Tokyo, Japan, 1998.
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© 1998 Springer-Verlag Berlin Heidelberg
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Zhang, X., Narita, T., Numao, M. (1998). Toward Effective Knowledge Acquisition with First Order Logic Induction. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_40
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DOI: https://doi.org/10.1007/3-540-49292-5_40
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