Encyclopedia of the Sciences of Learning

2012 Edition
| Editors: Norbert M. Seel

Inferential Theory of Learning

  • Kenneth A. KaufmanEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1428-6_1787



The Inferential Theory of Learning (ITL) is a means of classifying and understanding learning processes, both cognitive and machine, by the types of inference they make and by the way knowledge is created and transformed through learning. Such a view of learning processes is in contrast to the Computational Theory of Learning, in which learning strategies are organized by their computational complexity. Through the Inferential Theory, a learning process can be described as one or more knowledge transmutations (e.g., induction, abstraction, similization).

Theoretical Background

The Inferential Theory of Learning (ITL) was proposed by Michalski (1991, 1994) as a unified framework for developing and implementing multistrategy learning systems. ITL recognizes a learning process as consisting of three components: the input facts, the background knowledge, and the inference strategy being employed to generate new knowledge and thereby enhance...

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  1. Michalski, R. S. (1991). Inferential learning theory as a basis for multistrategy task-adaptive learning. Proceedings of the First International Workshop on Multistrategy Learning (pp. 3–18). Harpers Ferry, WV.Google Scholar
  2. Michalski, R. S. (1994). Inferential learning theory: developing foundations for multistrategy learning. In R. S. Michalski & G. Tecuci (Eds.), Machine learning – A multistrategy approach (pp. 3–61). San Mateo: Morgan Kaufmann.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Machine Learning and Inference LaboratoryGeorge Mason UniversityFairfaxUSA