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Definition
AQ learning is a form of supervised machine learning of rules from examples and background knowledge performed by the well-known AQ family of programs and other machine learning methods. AQ learning pioneered separate-and-conquer approach to rule learning in which examples are sequentially covered until a complete class description is formed. Derived knowledge is represented in a highly expressive form of attributional rules.
Theoretical Background
The core of AQ learning is a simple version of Aq (algorithm quasi-optimal) covering algorithm, developed by Ryszard S. Michalski in the late 1960s (Michalski 1969). The algorithm was initially developed for the purpose of minimization of logic functions, and later adapted for rule learning and other machine learning applications.
Simple Aq Algorithm
Aq algorithm realizes a form of supervised learning. Given a set of positive events (examples) P, a set of negative events N, and a...
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References
Kaufman, K., & Michalski, R. S. (2005). From data mining to knowledge mining. In C. R. Rao, J. L. Solka, & E. J. Wegman (Eds.), Handbook in statistics, vol. 24: Data mining and data visualization (pp. 47–75). North Holland: Elsevier.
Michalski, R. S. (1969). On the quasi-minimal solution of the general covering problem. Proceedings of the V international symposium on information processing (FCIP 69) (Switching Circuits), vol. A3. Yugoslavia, Bled, pp. 125–128, October 8–11.
Michalski, R. S. (2004). Attributional calculus: a logic and representation language for natural induction. Reports of the Machine Learning and Inference Laboratory, MLI 04–2, George Mason University, Fairfax, VA.
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© 2012 Springer Science+Business Media, LLC
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Wojtusiak, J. (2012). AQ Learning. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_845
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