Abstract
Traditionally, inductive learning algorithms such as decision tree learners have employed attribute-value representations, which are essentially propositional. While learning in first-order logic has been stud- ied for almost 20 years, this has mostly resulted in completely new learning algorithms rather than first-order upgrades of propositional learning algorithms. To re-establish the link between propositional and first-order learning, we have to focus on individual-centered representations. This short paper is devoted to the nature of first-order individual-centered representations for inductive learning. I discuss three possible perspecves: representing individuals as Herbrand interpretations, representing datasets as an individual-centered database, and representing individuals as terms.
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Flach, P.A. (1999). Knowledge Representation for Inductive Learning. In: Hunter, A., Parsons, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1999. Lecture Notes in Computer Science(), vol 1638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48747-6_15
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DOI: https://doi.org/10.1007/3-540-48747-6_15
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