Abstract
The power of an intelligent system lies in its knowledge. Much of the work in artificial intelligence has been on the acquisition of such knowledge for intelligent systems (e.g., Davis and Lenat, 1982; Marcus, 1988). This work addresses the task of inferring knowledge by generalization—forming general rules from specific cases. Cases are viewed as examples of some unknown concept, and the problem is thus to find a definition of the concept given known positive and negative examples of the concept. This concept learning approach to knowledge acquisition has received much attention in the machine learning community over the years (Buchanan and Mitchell, 1978; Michalski and Chilausky, 1980; Mitchell et al., 1983; Quinlan, 1983).
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© 1990 Kluwer Academic Publishers
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Hirsh, H. (1990). Overview. In: Incremental Version-Space Merging: A General Framework for Concept Learning. The Kluwer International Series in Engineering and Computer Science, vol 104. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1557-5_1
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DOI: https://doi.org/10.1007/978-1-4613-1557-5_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-8834-3
Online ISBN: 978-1-4613-1557-5
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