Attribute-Value and Relational Learning: A Statistical Viewpoint

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this extended abstract, rather than crossing the boundary between attribute-value and relational learning, we place ourselves above any such boundary and look down on the problem from the point of view of general principles of statistical inference. We do not pretend that this paper gives a full account of all relevant issues, but argue that starting from this generalised viewpoint and working down towards actual learning problems (e.g. decision tree learning, regression, ILP, etc) makes it easier to find the essential contrasts and similarities between different learning problems. Our primary goal (not achieved here) is to abstract away from superficial issues, such as the concrete syntactic representation of a problem or worse the sociological origin of an approach.
Original languageEnglish
Title of host publicationProceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries
EditorsLuc De Raedt, Stefan Kramer
Number of pages5
Publication statusPublished - 2000

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