Knowledge representation for inductive learning

Peter A. Flach. In Anthony Hunter and Simon Parsons, editors, Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU'99), volume 1638 of Lecture Notes in Artificial Intelligence, pages 160--167. Springer-Verlag, July 1999. More behind this link.

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.

BibTeX entry.

Knowledge representation for inductive learning

Abstract

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