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• W1536918907 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 studied 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 perspectives: representing individuals as Herbrand interpretations, representing datasets as an individual-centered database, and representing individuals as terms." @default.
• W1536918907 created "2016-06-24" @default.
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• W1536918907 date "1999-01-01" @default.
• W1536918907 modified "2023-09-26" @default.
• W1536918907 title "Knowledge Representation for Inductive Learning" @default.
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• W1536918907 doi "https://doi.org/10.1007/3-540-48747-6_15" @default.
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