Thesis: clustering and instance based learning in first order logic

Authors:
Jan Ramon
Affiliations:
K.U.Leuven, Department of Computer Science, Celestijnenlaan 200A, 3001 Heverlee, Belgium
Venue:
AI Communications
Year:
2002

Citing 2
Cited 3

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Transactions on computational collective intelligence IV

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Abstract

Instance based learning and clustering are popular methods in propositional machine learning. Both methods use a notion of similarity between objects. This dissertation investigates these methods in a relational setting. First, a number of new metrics are proposed. Next, these metrics are used to upgrade clustering and instance based learning to first order logic.