Abstract
Previous research in Machine Learning suggests that the process of concept formation can be divided into three distinct components. The first of these — aggregation — involves grouping instances of experience into collections. The second component — characterization — involves generating a description of the instances in the aggregate. The final process — utilization — consists of making use of the resulting description. These components are examined in more detail in this paper and the derivation of functional properties of a real—world environment is discussed within this framework.
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© 1986 Kluwer Academic Publishers
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Easterlin, J.D. (1986). Functional Properties and Concept Formation. In: Machine Learning. The Kluwer International Series in Engineering and Computer Science, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2279-5_14
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DOI: https://doi.org/10.1007/978-1-4613-2279-5_14
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-9406-1
Online ISBN: 978-1-4613-2279-5
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