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Explanation-Based Learning

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Encyclopedia of Machine Learning and Data Mining

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Synonyms

Analytical learning; Deductive learning; EBL; Utility problem

Definition

Explanation-based learning (EBL) is a principled method for exploiting available domain knowledge to improve supervised learning. Improvement can be in speed of learning, confidence of learning, accuracy of the learned concept, or a combination of these. In modern EBL the domain theory represents an expert’s approximate knowledge of complex systematic world behavior. It may be imperfect and incomplete. Inference over the domain knowledge provides analyticevidence that compliments the empirical evidence of the training data. By contrast, in original EBL, the domain theory is required to be much stronger; inferred properties are guaranteed. Another important aspect of modern EBL is the interaction between domain knowledge and labeled training examples afforded by explanations. Interaction allows the nonlinear combination of evidence so that the resulting information about the target concept can be much...

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Author information

Authors and Affiliations

  1. University of Illinois at Urbana, Urbana, IL, USA

    Gerald DeJong

  2. University of Illinois, Champaign, IL, USA

    ShiauHong Lim

Authors
  1. Gerald DeJong
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  2. ShiauHong Lim
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Corresponding author

Correspondence to Gerald DeJong .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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DeJong, G., Lim, S. (2017). Explanation-Based Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_96

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