Learning by discovering concept hierarchies
نویسندگان
چکیده
منابع مشابه
Learning by Discovering Concept Hierarchies
We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of switching circuits. To cope with high...
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Sequential pattern mining is the method that has received much attention in sequence data mining research and applications, however, a drawback is that it does not profit from prior knowledge of domains. In our previous work, we proposed a belief-driven method with fuzzy set theory for discovering the unexpected sequences that contradict existing knowledge of data, including occurrence constrai...
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With the advent of semantic web, various tools and techniques have been introduced for presenting and organizing knowledge. Concept hierarchies are one such technique which gained significant attention due to its usefulness in creating domain ontologies that are considered as an integral part of semantic web. Automated concept hierarchy learning algorithms focus on extracting relevant concepts ...
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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, al...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 1999
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(99)00008-9