Functional Properties and Concept Formation
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.
KeywordsMachine Learn Functional Property Learning System Concept Formation World State
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