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.
|Number of pages||5|
|Publication status||Published - 2000|