A Model For Generalization Based On Confirmatory Induction

ECML'97: Proceedings of the 9th European Conference on Machine Learning(1997)

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摘要
Confirmatory induction is based on the assumption that unknown individuals are similar to known ones, i.e. they satisfy the properties shared by known individuals. This assumption can be represented inside a non-monotonic logical framework. Accordingly, existing approaches to confirmatory induction take advantage of the machinery developed so far for non-monotonic inference. However, they are based on completion policies that are unnecessary strong for the induction purpose. The contribution of this paper is twofold: some basic requirements that any model for generalization based on confirmatory induction should satisfy are proposed. Then, a model for generalization based on Hempel's notion of confirmation is introduced. This model is rational in the sense that it satisfies the rationality postulates we exhibit; moreover, the completion principle on which this model is based captures exactly the similarity assumption, hence the model can be considered minimal as well.
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Confirmatory Induction
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