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Knowledge representation for inductive learning

Peter A. Flach, Knowledge representation for inductive learning. Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU'99). Anthony Hunter, Simon Parsons, (eds.). ISBN 3-540-66131-X, pp. 160–167. July 1999. No electronic version available. External information

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.

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