concept learning A major topic in machine learning that addresses the problem of classifying a series of data instances as equivalent on the basis of some given properties. The idea is to locate clusters within sets of data, using methods of induction, so that the clusters define and distinguish salient features within the data. The clusters represent “concepts” and their descriptions define a class of data that fits a particular conceptual cluster.
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