Skip to main content
  • 452 Accesses

Synonyms

Algorithm quasi-optimal; Star-learning

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...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 3,400.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 2,999.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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.

    Google Scholar 

  • 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 

  • 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Janusz Wojtusiak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this entry

Cite this entry

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

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-1428-6_845

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-1427-9

  • Online ISBN: 978-1-4419-1428-6

  • eBook Packages: Humanities, Social Sciences and Law

Publish with us

Policies and ethics