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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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DeJong, G., Lim, S.H. (2011). Explanation-Based Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_296
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DOI: https://doi.org/10.1007/978-0-387-30164-8_296
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