Abstract:
Abstract. A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g. classification or conceptual clustering, via robust data analysis algorithms (e.g. k-nearest neighbors or k-means). A method for building a distance on first-order logic domains is presented in this paper. The distance is constructed from examples expressed as definite or constrained clauses, via a two-step process: a set of d hypotheses is first learnt from the training examples. These hypotheses serve as new descriptors of the problem domain Lh: they induce a mapping � � from Lh onto the space of integers IN
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