Abstract
The ability to generalize rules from examples is well known as an essential capability of a learning system. Generalization involves observing a set of training examples of some general concepts, identifying the essential features common to these examples, and then formulating a concept definition based on these common features. In this article we describe a new generalization method in order to construct from a large set of examples a rule base describing the concepts which can be used by an expert system to recognize unknown objects in a particular domain.
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