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Learning two-tiered descriptions of flexible concepts: The POSEIDON system
1992
Machine Learning
This paper describes a method for learning flexible concepts, by which are meant concepts that lack precise definition and are context-dependent. To describe such concepts, the method employs a two-tiered representation, in which the first tier captures explicitly basic concept properties, and the second tier characterizes allowable concept's modifications and context dependency. In the proposed method, the first tier, called Base Concept Represen-t~ation (BCR), is created in two phases. In
doi:10.1007/bf00994004
fatcat:ygya7a243ndshjmb7zo6nltcgu