Unsupervised Learning of Probabilistic Concept Hierarchies
نویسندگان
چکیده
Fisher's Cobweb provided a well-deened framework for research on the unsupervised induction of probabilistic concept hierarchies. The system also sparked the development of many successors that extended this framework along various dimensions. In this paper, we summarize the assumptions that Cobweb embodies about the representation, organization, use, and formation of probabilistic concepts, along with experimental studies that examine its sources of power. After this, we consider three systems { Arachne, Twilix, and Oxbow { that incorporate signiicant extensions and present empirical evidence that these improve behavior. In closing, we discuss other paradigms for the unsupervised learning of probabilistic knowledge and their relation to the Cobweb framework.
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