Encyclopedia of the Sciences of Learning

2012 Edition
| Editors: Norbert M. Seel

AQ Learning

  • Janusz WojtusiakEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1428-6_845

Synonyms

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

  1. 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.Google Scholar
  2. 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.Google Scholar
  3. 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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Machine Learning and Inference Laboratory, College of Health and Human ServicesGeorge Mason UniversityFairfaxUSA